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Combinatorics·Captain: mikedeng1

Generalized Turán Problems for Even Cycles 3: The Maximum Number of Six-Cycles in a Bipartite C_8-Free Graph on n Vertices Is n³ + O(n^{5/2})Research Paper

Motivation

The generalized Turán number ex(n,H,F)\mathrm{ex}(n,H,\mathcal F)ex(n,H,F) is the largest number of copies of a graph HHH in an nnn-vertex graph containing no member of a family F\mathcal FF. With H=K2H=K_2H=K2​ it is the classical Turán number; the systematic study of general HHH began with Alon and Shikhelman (2016), and cycles are its most studied case: Bollobás and Győri bounded ex(n,C3,C5)\mathrm{ex}(n,C_3,C_5)ex(n,C3​,C5​), and Gerbner and Palmer determined ex(n,Cl,K2,t)\mathrm{ex}(n,C_l,K_{2,t})ex(n,Cl​,K2,t​).

Gerbner, Győri, Methuku and Vizer, Generalized Turán problems for even cycles (J. Combin. Theory Ser. B, 2020; arXiv:1712.07079), count copies of one cycle in graphs avoiding another. For even cycles they show ex(n,C2l,C2k)=Θ(nl)\mathrm{ex}(n,C_{2l},C_{2k})=\Theta(n^l)ex(n,C2l​,C2k​)=Θ(nl). Most extremal constructions there are bipartite, so they also study the bipartite generalized Turán number. For the first non-trivial pair, six-cycles in a graph avoiding eight-cycles, they determine its leading term exactly. This mission formalizes that result, Theorem 12 of the paper.

Setting

All graphs are finite and simple. A kkk-cycle CkC_kCk​ is a cycle with kkk vertices. A copy of CkC_kCk​ in GGG is a subgraph of GGG isomorphic to CkC_kCk​; N(Ck,G)\mathcal N(C_k,G)N(Ck​,G) is the number of copies, so a cycle is counted once regardless of its starting point and direction. A graph is C8C_8C8​-free if no subgraph of it is isomorphic to C8C_8C8​ (induced or not). A graph is bipartite if its vertices can be split into two classes AAA and BBB with every edge joining AAA to BBB.

exbip(n,C6,C8)=max⁡{N(C6,G):G bipartite, C8-free, ∣V(G)∣=n}.\mathrm{ex}_{bip}(n,C_6,C_8)=\max\{\mathcal N(C_6,G) : G \text{ bipartite},\ C_8\text{-free},\ |V(G)|=n\}.exbip​(n,C6​,C8​)=max{N(C6​,G):G bipartite, C8​-free, ∣V(G)∣=n}.

The proof works with a fixed bipartition (A,B)(A,B)(A,B) of a C8C_8C8​-free graph GGG and uses the following notions.

  • N(u,v)N(u,v)N(u,v) is the set of common neighbours of uuu and vvv.
  • A pair {u,v}\{u,v\}{u,v} is fat if ∣N(u,v)∣≥4|N(u,v)|\ge 4∣N(u,v)∣≥4.
  • A 6-cycle is fat if it contains a fat pair from each class.
  • A pair is marked if it has exactly three common neighbours.
  • A 4-set F⊆AF\subseteq AF⊆A is nice if its four vertices have three common neighbours in BBB, i.e. they span a K4,3K_{4,3}K4,3​.
  • For x,y,z∈Ax,y,z\in Ax,y,z∈A, h(x,y,z)h(x,y,z)h(x,y,z) is the number of 6-cycles through all three. The triple is good if h≤6h\le 6h≤6 and bad otherwise.
  • A hypergraph is Berge-C4C_4C4​-free if it has no distinct vertices x,y,z,wx,y,z,wx,y,z,w and distinct hyperedges h1,…,h4h_1,\dots,h_4h1​,…,h4​ with {x,y}⊆h1\{x,y\}\subseteq h_1{x,y}⊆h1​, {y,z}⊆h2\{y,z\}\subseteq h_2{y,z}⊆h2​, {z,w}⊆h3\{z,w\}\subseteq h_3{z,w}⊆h3​, {w,x}⊆h4\{w,x\}\subseteq h_4{w,x}⊆h4​.

In Lean, exbip(n,C6,C8)\mathrm{ex}_{bip}(n,C_6,C_8)exbip​(n,C6​,C8​) is exBip n (cycleGraph 6) {8}, and N\mathcal NN is Mathlib's copyCount.

Formalization targets

Goal: Theorem 12

exbip(n,C6,C8)=n3+O(n5/2)(n→∞).\mathrm{ex}_{bip}(n,C_6,C_8)=n^3+O\bigl(n^{5/2}\bigr)\qquad(n\to\infty).exbip​(n,C6​,C8​)=n3+O(n5/2)(n→∞).

This is two-sided: ∣exbip(n,C6,C8)−n3∣≤Cn5/2|\mathrm{ex}_{bip}(n,C_6,C_8)-n^3|\le Cn^{5/2}∣exbip​(n,C6​,C8​)−n3∣≤Cn5/2 for some constant CCC and all large nnn.

Milestones

  1. Lower bound. K3,n−3K_{3,n-3}K3,n−3​ is bipartite, C8C_8C8​-free, and has exactly 6(n−33)=n3−O(n2)6\binom{n-3}{3}=n^3-O(n^2)6(3n−3​)=n3−O(n2) six-cycles. This is Theorem 10's construction with k=4k=4k=4, l=3l=3l=3, and Remark 2 notes that it is bipartite.
  2. Shape of fat 6-cycles.
    • Claim 3: a fat pair {v1,v3}\{v_1,v_3\}{v1​,v3​} on v1⋯v6v_1\cdots v_6v1​⋯v6​ excludes the fat pairs {v2,v4}\{v_2,v_4\}{v2​,v4​} and {v2,v6}\{v_2,v_6\}{v2​,v6​}.
    • Claim 4: fat 6-cycles through one fat pair have pairwise intersecting fat pairs in the other class.
    • Claim 5: distinct fat pairs of fat 6-cycles in AAA have ∣N∩N′∣≤1|N\cap N'|\le 1∣N∩N′∣≤1.
    • Observation 1: a disjointness statement for fat pairs sharing a vertex.
  3. Claim 6. For a fat pair {v1,v3}⊆A\{v_1,v_3\}\subseteq A{v1​,v3​}⊆A, the sum g(v1,v3)g(v_1,v_3)g(v1​,v3​) of ∣N(v4,v6)∣|N(v_4,v_6)|∣N(v4​,v6​)∣ over the fat pairs {v4,v6}⊆B\{v_4,v_6\}\subseteq B{v4​,v6​}⊆B on a common fat 6-cycle with v1,v3v_1,v_3v1​,v3​ is at most 4n4n4n.
  4. Corollary 7 (from Győri–Lemons). A Berge-C4C_4C4​-free hypergraph on nnn vertices has ∑e∣e∣=O(n3/2)\sum_e|e|=O(n^{3/2})∑e​∣e∣=O(n3/2).
  5. Claim 7. The hypergraph on BBB whose hyperedges are the sets N(v1,v3)N(v_1,v_3)N(v1​,v3​), over the fat pairs of AAA that lie on a fat 6-cycle, is Berge-C4C_4C4​-free.
  6. Claim 8. There are O(n5/2)O(n^{5/2})O(n5/2) fat 6-cycles.
  7. Non-fat cycles.
    • Claim 9: a marked pair inside a nice set gives good triples.
    • Claim 10: O(∣A∣3/2)O(|A|^{3/2})O(∣A∣3/2) marked pairs lie in no nice set.
    • Claim 11: O(∣A∣2)O(|A|^2)O(∣A∣2) bad triples contain neither a fat nor a marked pair.
  8. One-sided count. At most 6(∣A∣3)+O(∣A∣5/2)6\binom{|A|}{3}+O(|A|^{5/2})6(3∣A∣​)+O(∣A∣5/2) six-cycles contain no fat pair from AAA.

Significance

Theorem 12 is the only case of the paper where a bipartite generalized Turán number of two even cycles is determined up to a lower-order term. Theorem 10 gives the order nln^lnl in general, and Remark 2 gives bipartite upper and lower bounds whose ratio tends to 1 only as k→∞k\to\inftyk→∞. Theorem 12 settles (l,k)=(3,4)(l,k)=(3,4)(l,k)=(3,4) exactly. The authors conjecture that the non-bipartite number ex(n,C6,C8)\mathrm{ex}(n,C_6,C_8)ex(n,C6​,C8​) has the same asymptotics, but this is open. The argument also links graph counting to Berge hypergraphs: Claim 7 reduces the fat 6-cycles to a Berge-C4C_4C4​-free hypergraph, whose total edge size is controlled by Corollary 7.

Theorem 12 is proved in the paper. As far as a search of the Prove2Me catalogue shows, neither it nor any of its claims has a machine-checked proof. Formalizing it means:

  • formalizing the case analysis of §4.2;
  • formalizing Corollary 7, which depends on the Győri–Lemons theorems (Theorems 5 and 6), proved outside the paper;
  • formalizing the elementary but tedious count of 6-cycles in K3,n−3K_{3,n-3}K3,n−3​.

Difficulty

The obvious bound fails. Every 6-cycle in a bipartite graph has three vertices in each class, and each pair of them has some common neighbours. If no pair had more than three common neighbours, each triple in AAA would lie on at most 272727 six-cycles, giving roughly 27(n3)27\binom{n}{3}27(3n​) cycles, not n3n^3n3. The leading constant 111 needs two refinements. First, triples on more than 666 cycles must be shown to be rare. Second, cycles through pairs with many common neighbours (fat pairs) must be bounded separately, by O(n5/2)O(n^{5/2})O(n5/2). That second bound is the main obstacle. A fat pair alone can lie on many cycles, so counting fat pairs is not enough. What makes the bound work is the near-disjointness of the common neighbourhoods (Claims 4–6) together with a hypergraph Turán estimate.

Formalization scope

  • Vertices and copies. Graphs are SimpleGraph (Fin n), or a general finite type in milestones about a single graph. Copies are unlabelled (copyCount, the number of subgraphs isomorphic to C6C_6C6​): K3,3K_{3,3}K3,3​ has 666 copies of C6C_6C6​, not 727272.

  • Bipartiteness. In exbip\mathrm{ex}_{bip}exbip​ it is Colorable 2. In the milestones it is an explicit bipartition (A,B)(A,B)(A,B) that excludes edges inside a class. C8C_8C8​-freeness is (cycleGraph 8).Free G.

  • The maximum. exbip\mathrm{ex}_{bip}exbip​ is a supremum of natural numbers over a nonempty finite set, since the empty graph is admissible. It is therefore a true maximum; a sanity file proves this.

  • Cycles inside the proof. Labelled 6-cycles are injective maps from Z/6\mathbb Z/6Z/6 with consecutive images adjacent. Claim 8 counts fat 6-cycles labelled, which changes only the constant.

  • Landau terms. Every O(⋅)O(\cdot)O(⋅) is an explicit constant, quantified before nnn and the graph. The goal uses Mathlib's IsBigO of exbip(n,C6,C8)−n3\mathrm{ex}_{bip}(n,C_6,C_8)-n^3exbip​(n,C6​,C8​)−n3 against n5/2n^{5/2}n5/2.

  • Corrections to the printed text.

    • Observation 1's conclusion is stated with N(v4,v6′)N(v_4,v_6')N(v4​,v6′​), as in its proof, rather than the printed N(v4′,v6′)N(v_4',v_6')N(v4′​,v6′​).
    • Claim 6's ggg is the displayed sum, not "the cardinality of the union".
    • Claim 4 drops the hypothesis "different cycles", which only excludes a trivial case. It also drops "these are the other fat pairs", which follows from Claim 3.
  • Ruled-out encodings. Some encodings would make the statements trivial or wrong, and none of them is used:

    • a one-sided goal (only the upper bound, or only the lower bound);
    • a junk supremum;
    • labelled copies in place of unlabelled ones;
    • constants allowed to depend on nnn or on the graph;
    • a bipartition that allows edges inside a class.
  • Infrastructure. A complete development needs:

    • counting of cycle copies in complete bipartite graphs;
    • Hall's theorem (in Mathlib), used in Claims 7 and 10;
    • the Kővári–Sós–Turán bound ex(m,C4)=O(m3/2)\mathrm{ex}(m,C_4)=O(m^{3/2})ex(m,C4​)=O(m3/2) for Claim 10;
    • the Győri–Lemons theorems for Corollary 7.

    The Berge-cycle and C4C_4C4​-free bounds can be reused beyond this mission. Contributions are welcome at any level: proofs of individual claims, of Corollary 7, or the final assembly.

Selected references

  • D. Gerbner, E. Győri, A. Methuku, M. Vizer, Generalized Turán problems for even cycles, J. Combin. Theory Ser. B 145 (2020); arXiv:1712.07079v3. https://arxiv.org/abs/1712.07079, https://doi.org/10.1016/j.jctb.2020.05.005
  • N. Alon, C. Shikhelman, Many TTT copies in HHH-free graphs, J. Combin. Theory Ser. B 121 (2016). https://doi.org/10.1016/j.jctb.2016.03.004
  • E. Győri, N. Lemons, Hypergraphs with no cycle of a given length, Combin. Probab. Comput. 21 (2012). https://doi.org/10.1017/S0963548311000691
  • B. Bollobás, E. Győri, Pentagons vs. triangles, Discrete Math. 308 (2008). https://doi.org/10.1016/j.disc.2007.08.041
  • D. Gerbner, C. Palmer, Counting copies of a fixed subgraph in FFF-free graphs, European J. Combin. 82 (2019). https://doi.org/10.1016/j.ejc.2019.07.004
16 thms1 active userReviewed
Combinatorics·Captain: mikedeng1

Generalized Turán Problems for Even Cycles 4: For k > l and 2k ≠ ml, the Maximum Number of ml-Cycles in a Graph with No Cycle of Length 3, …, 2l−1 or 2k Is Θ(n^m)Research Paper

Motivation

The Turán number ex(n,F)\mathrm{ex}(n,F)ex(n,F) is the largest number of edges in an nnn-vertex graph with no copy of FFF. Its generalized version, introduced systematically by Alon and Shikhelman (JCTB 2016), replaces edges by copies of a second graph HHH: ex(n,H,F)\mathrm{ex}(n,H,\mathcal F)ex(n,H,F) is the largest number of copies of HHH in an nnn-vertex graph containing no member of the family F\mathcal FF. When both HHH and the forbidden graphs are cycles, these numbers measure how many long cycles a graph can carry while some cycle lengths are excluded and they refine the Bondy–Simonovits even-cycle theorem (JCTB 1974), which bounds the number of edges of a C2kC_{2k}C2k​-free graph by O(n1+1/k)O(n^{1+1/k})O(n1+1/k).

Gerbner, Győri, Methuku and Vizer (arXiv:1712.07079, J. Combin. Theory Ser. B 2020, DOI 10.1016/j.jctb.2020.05.005) study ex(n,Ca,CA)\mathrm{ex}(n,C_a,\mathcal C_A)ex(n,Ca​,CA​) for cycles. Their Theorem 10 gives ex(n,C2l,C2k)=Θ(nl)\mathrm{ex}(n,C_{2l},C_{2k})=\Theta(n^l)ex(n,C2l​,C2k​)=Θ(nl); Theorem 14, the subject of this mission, adds a girth condition: when all cycles of length 3,…,2l−13,\dots,2l-13,…,2l−1 are forbidden together with C2kC_{2k}C2k​, the order of magnitude of the number of CmlC_{ml}Cml​'s is nmn^mnm. The case m=2m=2m=2 answers the question of how many C2lC_{2l}C2l​'s a C2kC_{2k}C2k​-free graph of girth 2l2l2l can have.

Setting

All graphs are finite and simple. A copy of a graph HHH in GGG is a subgraph of GGG isomorphic to HHH, and N(H,G)\mathcal N(H,G)N(H,G) is the number of copies. CaC_aCa​ is the cycle of length a≥3a\ge3a≥3; PlP_lPl​ is the path on lll vertices (of length l−1l-1l−1).

For a set AAA of integers, each at least 333, let CA={Ca:a∈A}\mathcal C_A=\{C_a:a\in A\}CA​={Ca​:a∈A}; a graph is CA\mathcal C_ACA​-free if it contains none of these cycles as a subgraph. Write C2l−1={C3,C4,…,C2l−1}\mathcal C_{2l-1}=\{C_3,C_4,\dots,C_{2l-1}\}C2l−1​={C3​,C4​,…,C2l−1​}. The generalized Turán number is

ex(n,H,CA)=max⁡{N(H,G):G is a CA-free graph on n vertices}.\mathrm{ex}(n,H,\mathcal C_A)=\max\{\mathcal N(H,G): G\text{ is a }\mathcal C_A\text{-free graph on }n\text{ vertices}\}.ex(n,H,CA​)=max{N(H,G):G is a CA​-free graph on n vertices}.

For vertices a≠ba\neq ba=b, fl(a,b)f_l(a,b)fl​(a,b) is the number of paths with lll edges between aaa and bbb. A pair {u,v}\{u,v\}{u,v} is fat if fl(u,v)≥4l2f_l(u,v)\ge 4l^2fl​(u,v)≥4l2, and a copy of C2lC_{2l}C2l​ is fat if all lll of its pairs of opposite vertices are fat.

The (l,t)(l,t)(l,t)-theta graph joins two vertices by ttt internally disjoint paths of length lll. For a graph FFF, the theta-(n,F,l)(n,F,l)(n,F,l) graph replaces each edge of FFF by an (l,t)(l,t)(l,t)-theta graph with t=⌊(n−∣V(F)∣)/(∣E(F)∣(l−1))⌋t=\lfloor (n-|V(F)|)/(|E(F)|(l-1))\rfloort=⌊(n−∣V(F)∣)/(∣E(F)∣(l−1))⌋ and adds isolated vertices up to nnn; here F=CmF=C_mF=Cm​ for m≥3m\ge3m≥3 and F=K2F=K_2F=K2​ for m=2m=2m=2.

Formalization targets

Goal: Theorem 14

For integers k>l≥2k>l\ge2k>l≥2 and m≥2m\ge2m≥2 with 2k≠ml2k\neq ml2k=ml,

ex(n,Cml,C2l−1∪{C2k})=Θ(nm)(n→∞).\mathrm{ex}\big(n,C_{ml},\mathcal C_{2l-1}\cup\{C_{2k}\}\big)=\Theta(n^m)\qquad(n\to\infty).ex(n,Cml​,C2l−1​∪{C2k​})=Θ(nm)(n→∞).

Both bounds are part of the goal; the constants depend on k,l,mk,l,mk,l,m only.

Milestones

  1. Lemmas 25 and 26: forbidding additional odd cycles (Lemma 25) or shorter odd cycles (Lemma 26) does not change the order of magnitude of ex(n,C2k,CA)\mathrm{ex}(n,C_{2k},\mathcal C_A)ex(n,C2k​,CA​) or ex(n,C2k+1,CA)\mathrm{ex}(n,C_{2k+1},\mathcal C_A)ex(n,C2k+1​,CA​).
  2. Lemma 27: a partition V1,…,VsV_1,\dots,V_sV1​,…,Vs​ with no P3P_3P3​ inside ViV_iVi​ (i<si<si<s), no Pp+1P_{p+1}Pp+1​ joining different parts and VsV_sVs​ independent forces ∣E(G)∣≤p n|E(G)|\le p\,n∣E(G)∣≤pn.
  3. The lower bound: the theta graph has only cycles of lengths 2l2l2l and mlmlml, hence avoids the forbidden family, and has Ω(nm)\Omega(n^m)Ω(nm) cycles of length mlmlml.
  4. Claims 12 and 13: the structure of two 2l2l2l-cycles through a common path, and short detours between fat pairs, in graphs of girth at least 2l2l2l.
  5. Counting C2lC_{2l}C2l​'s in girth at least 2l2l2l: at most (4l2−12)(n2)\binom{4l^2-1}{2}\binom n2(24l2−1​)(2n​) non-fat ones; Claim 14 on the greedy multiplicities; at most 2k(k−l)l−2(n2)\frac{2k(k-l)}{l-2}\binom n2l−22k(k−l)​(2n​) fat ones.
  6. The case m≥3m\ge3m≥3: display (5), 12∑a≠b(fl(a,b)2)=O(n2)\tfrac12\sum_{a\ne b}\binom{f_l(a,b)}{2}=O(n^2)21​∑a=b​(2fl​(a,b)​)=O(n2), and Claim 15, ∑b≠afl(a,b)=O(n)\sum_{b\neq a}f_l(a,b)=O(n)∑b=a​fl​(a,b)=O(n).

Significance

Theorem 14 locates ex(n,Cml,C2l−1∪{C2k})\mathrm{ex}(n,C_{ml},\mathcal C_{2l-1}\cup\{C_{2k}\})ex(n,Cml​,C2l−1​∪{C2k​}) at nmn^mnm for every admissible triple, so in graphs of girth 2l2l2l forbidding one further even cycle C2kC_{2k}C2k​ (with 2k≠ml2k\neq ml2k=ml) caps the number of mlmlml-cycles at the count achieved by a blown-up mmm-cycle of long paths. The paper uses it to settle the order of magnitude of ex(n,C6,CA)\mathrm{ex}(n,C_6,\mathcal C_A)ex(n,C6​,CA​) for every family AAA with 4∈A4\in A4∈A, 6∉A6\notin A6∈/A and ∣Ae∣≥2|A_e|\ge2∣Ae​∣≥2 (Theorem 20), and its case l=2l=2l=2 recovers Theorem 10.

The result is proved in the paper; nothing here is open mathematically. To the best of a platform search (2026-10-08), no part of it is formalized: Mathlib has copyCount, cycleGraph and pathGraph, but no generalized Turán numbers, no theta graphs and no counting of paths between two vertices. The mission produces a machine-checked proof of the theorem and of the counting lemmas of §5, which are reusable for other generalized Turán problems with cycles.

Difficulty

The lower bound is an explicit construction, and its difficulty is bookkeeping: proving that a subdivided multigraph has no cycles other than those of lengths 2l2l2l and mlmlml, and counting the mlmlml-cycles. The upper bound is the substance. A direct count of C2lC_{2l}C2l​'s through codegrees, which works for C4C_4C4​, fails here because paths of length lll between a pair are not controlled by a single neighbourhood. The paper's route separates fat from non-fat cycles and runs a greedy multiplicity procedure; Claim 14 then grows a C2kC_{2k}C2k​ by an induction on the cycle length in steps of l−1l-1l−1, which needs the precise intersection pattern of Claim 12 and the detour paths of Claim 13. For m≥3m\ge3m≥3 the argument goes through layered neighbourhoods and Lemma 27. The m=2m=2m=2 argument divides by l−2l-2l−2, so for l=2l=2l=2 a different route (Theorems 10 and 11 of the paper) is needed.

Formalization scope

Graphs are SimpleGraph (Fin n) for the extremal function and graphs on an arbitrary finite type for the lemmas. Copies are unlabelled (copyCount, subgraphs isomorphic to the pattern); CaC_aCa​ and PlP_lPl​ are Mathlib's cycleGraph a and pathGraph l. exCyc n H A is the supremum, over CA\mathcal C_ACA​-free graphs on Fin n, of the copy count; the set is finite and contains the empty graph's count whenever every length in AAA is at least 333, so it is a maximum. The forbidden family of the goal is Set.Icc 3 (2 * l - 1) ∪ {2 * k}, every length from 333 to 2l−12l-12l−1 together with 2k2k2k. Θ\ThetaΘ, OOO and Ω\OmegaΩ are Mathlib's asymptotic relations along atTop, with constants fixed before nnn and the graph. fl(a,b)f_l(a,b)fl​(a,b) counts injective vertex sequences (paths, not walks). The theta graph thetaCycle n m l is an explicit graph on Fin n.

Added or pinned hypotheses, all disclosed in the items: l≥2l\ge2l≥2 in the goal (the page writes only k>lk>lk>l; at l=1l=1l=1 the family C2l−1\mathcal C_{2l-1}C2l−1​ is empty and the statement fails); C≠C′C\neq C'C=C′ in Claim 12; l≥3l\ge3l≥3 where the page divides by l−2l-2l−2; Lemma 27 with the explicit bound p np\,npn of its proof, which is uniform in sss; Claim 15 under the full family C2l−1∪{C2k}\mathcal C_{2l-1}\cup\{C_{2k}\}C2l−1​∪{C2k​}, which its proof needs. Claim 14 assumes only girth ≥2l\ge 2l≥2l, not C2kC_{2k}C2k​-freeness, since on the page its conclusion at r=kr=kr=k is the contradiction.

The statements rule out the trivializing readings: the goal is two-sided, so an upper bound alone does not close it; exCyc is a genuine maximum, not a junk supremum; copies are unlabelled; the constants come before nnn and the graph; the forbidden family contains all short cycles, not only C2l−1C_{2l-1}C2l−1​.

A complete development needs path counting in graphs of large girth (uniqueness of short paths), layered neighbourhoods, the degeneracy argument of Lemma 27, a random bipartition argument for Lemma 25, and cycle structure in theta graphs. Contributions of any milestone, and of general-purpose lemmas on paths and cycles in SimpleGraph, are welcome.

Selected references

  • D. Gerbner, E. Győri, A. Methuku, M. Vizer, Generalized Turán problems for even cycles, J. Combin. Theory Ser. B (2020); arXiv:1712.07079v3 (2018). https://arxiv.org/abs/1712.07079, https://doi.org/10.1016/j.jctb.2020.05.005
  • N. Alon, C. Shikhelman, Many T copies in H-free graphs, J. Combin. Theory Ser. B 121 (2016). https://doi.org/10.1016/j.jctb.2016.03.004
  • J. A. Bondy, M. Simonovits, Cycles of even length in graphs, J. Combin. Theory Ser. B 16 (1974). https://doi.org/10.1016/0095-8956(74)90052-5
14 thms1 active userReviewed
Combinatorics·Captain: mikedeng1

Generalized Turán Problems for Even Cycles 7: The Maximum Number of Paths on l Vertices in a C_{2k+1}-Free Graph on n Vertices Is (1+o(1))(n/2)^lResearch Paper

Motivation

An extremal graph problem asks how many copies of a chosen small graph can occur when a different graph is forbidden. The usual Turán problem counts edges; counting paths or cycles gives a more detailed view of the structure that can survive a forbidden subgraph. Gerbner, Győri, Methuku and Vizer study these generalized Turán numbers for paths and cycles in their paper on even-cycle problems. Their Theorem 23 asks for the leading number of paths when a specified odd cycle is absent. The answer has the same leading term as a balanced complete bipartite graph, despite the fact that excluding one odd cycle is weaker than requiring a graph to be bipartite. Gerbner et al., Theorem 23

The spectral theorem cited by the authors gives a useful threshold for this setting. For a graph with no cycle of length 2k+12k+12k+1, it bounds the adjacency spectral radius once the vertex count exceeds 320(2k+1)320(2k+1)320(2k+1). This threshold is explicit and stays visible in the finite-size milestone. Gerbner et al., Theorem 32, citing Nikiforov (2008).

Setting

A simple graph GGG has finitely many vertices and no loops or parallel edges. For a≥3a\ge3a≥3, CaC_aCa​ is the cycle on aaa vertices, and PlP_lPl​ is the path on lll vertices. Thus PlP_lPl​ has l−1l-1l−1 edges, following the paper's convention. A copy of HHH in GGG is a subgraph of GGG isomorphic to HHH; copies are unlabelled, so reversing a path does not create a second copy. Write N(H,G)\mathcal N(H,G)N(H,G) for their number. Gerbner et al., pp. 2–3

For a set AAA of lengths, a graph is CA\mathcal C_ACA​-free if it has no subgraph isomorphic to CaC_aCa​ for any a∈Aa\in Aa∈A. The mission mainly uses A={2k+1}A=\{2k+1\}A={2k+1}: exactly one odd cycle length is forbidden, while shorter and longer cycles may still occur. The generalized Turán number is the maximum

ex⁡(n,H,CA)=max⁡∣V(G)∣=nG is CA-freeN(H,G).\operatorname{ex}(n,H,\mathcal C_A) =\max_{\substack{|V(G)|=n\\G\text{ is }\mathcal C_A\text{-free}}}\mathcal N(H,G).ex(n,H,CA​)=∣V(G)∣=nG is CA​-free​max​N(H,G).

The adjacency matrix AGA_GAG​ records whether each pair of vertices is adjacent. Because the graph is undirected, this is a real symmetric matrix. Its spectral radius μ(G)\mu(G)μ(G) is the largest absolute value of an eigenvalue of AGA_GAG​. The graph Bn,aB_{n,a}Bn,a​ is the complete bipartite graph with parts of sizes aaa and n−an-an−a; the balanced choice uses a=⌊n/2⌋a=\lfloor n/2\rfloora=⌊n/2⌋. Gerbner et al., §§7.1–7.2

Formalization targets

Main result: Theorem 23

For each fixed k≥1k\ge1k≥1 and l≥2l\ge2l≥2, as n→∞n\to\inftyn→∞,

ex⁡(n,Pl,C{2k+1})=(1+o(1))(n2)l.\operatorname{ex}(n,P_l,\mathcal C_{\{2k+1\}}) =(1+o(1))\left(\frac n2\right)^l.ex(n,Pl​,C{2k+1}​)=(1+o(1))(2n​)l.

The equality gives both sides of the asymptotic estimate. The o(1)o(1)o(1) term may depend on the fixed parameters k,lk,lk,l and tends to zero as the number of vertices grows. Gerbner et al., Theorem 23, p. 8

Supporting targets

The milestone list records the balanced complete bipartite lower bound, the comparison between paths and walks in equation (7), a spectral bound on the walk sum, Nikiforov's stated spectral estimate, and the resulting finite-size upper bound. In particular, the last two targets retain the paper's constants:

n>320(2k+1),μ(G)≤n2/4,N(Pl,G)≤(n/2)l.n>320(2k+1),\qquad \mu(G)\le\sqrt{n^2/4},\qquad \mathcal N(P_l,G)\le(n/2)^l.n>320(2k+1),μ(G)≤n2/4​,N(Pl​,G)≤(n/2)l.

The milestone statements identify which assertions concern every graph and which require the forbidden-cycle condition. Gerbner et al., pp. 30, 32

Companion: even cycles (Theorem 24)

For fixed k≥1k\ge1k≥1 and l≥2l\ge2l≥2,

ex⁡(n,C2l,C{2k+1})=(1+o(1)) 12l(n24)l,\operatorname{ex}(n,C_{2l},\mathcal C_{\{2k+1\}})=(1+o(1))\,\frac{1}{2l}\left(\frac{n^2}{4}\right)^{l},ex(n,C2l​,C{2k+1}​)=(1+o(1))2l1​(4n2​)l,

and Remark 3 records the inequality N(P2l,G)≥2l N(C2l,G)\mathcal N(P_{2l},G)\ge 2l\,\mathcal N(C_{2l},G)N(P2l​,G)≥2lN(C2l​,G) for every graph GGG, which links this count to Theorem 23. Gerbner et al., Theorem 24, p. 11; Remark 3, p. 32

Significance

Theorem 23 fixes the leading coefficient for the maximum path count for every fixed path length and forbidden odd-cycle length in its range. A balanced complete bipartite graph reaches that coefficient asymptotically, so the result identifies an extremal construction at first order. The lower-bound milestone formalizes this construction with balanced parts, including the one-vertex rounding needed when nnn is odd. The matrix and spectral milestones can also be reused when counting walks or paths under different graph restrictions. Gerbner et al., §7.2

The paper proves these results on paper. In this mission, the definitions and theorem statements are checked by Lean, while proofs of the draft theorems remain to be supplied. A completed development would make the path-count theorem and its stated spectral dependency machine checked in the pinned Mathlib environment. It would also expose where a general graph identity suffices and where the odd-cycle prohibition matters.

Difficulty

Counting paths directly is difficult because the available information about a graph with one forbidden odd-cycle length does not control each vertex degree tightly enough to determine a sharp path count. Counting walks is easier algebraically, but walks may revisit vertices, so their number is generally larger than the number of paths. A graph-wide spectral bound is needed to control the walk total sharply, and the cited Nikiforov estimate has to be carried with its forbidden-cycle hypothesis and vertex threshold. A generic bound based only on the maximum degree would not yield the coefficient in Theorem 23. Gerbner et al., §§7.1–7.2

Formalization scope

Graphs on nnn vertices are represented as SimpleGraph (Fin n). Mathlib's cycleGraph a and pathGraph l represent CaC_aCa​ and the path on lll vertices, and copyCount counts unlabelled subgraph copies. The finite set of real adjacency eigenvalues defines μ(G)\mu(G)μ(G) through a maximum of absolute values. On an empty vertex type this value is zero. The extremal number is a natural-number supremum over feasible graphs; for the odd-cycle family the feasible set is nonempty and finite, so this supremum is attained.

The goal has k≥1k\ge1k≥1 and l≥2l\ge2l≥2, exactly as Theorem 23 states. The lower-bound item chooses parts of sizes ⌊n/2⌋\lfloor n/2\rfloor⌊n/2⌋ and ⌈n/2⌉\lceil n/2\rceil⌈n/2⌉. Equation (7) requires l≥2l\ge2l≥2: for l=1l=1l=1 the claimed factor of two is false. The finite spectral and path bounds require n>320(2k+1)n>320(2k+1)n>320(2k+1). These conditions are explicit in the Lean statements. Natural subtraction appears only in expressions whose hypotheses make the intended quantity meaningful, including l−1l-1l−1; the bipartite part size is computed with natural division. The asymptotic statements use real sequences tending to zero and eventual comparisons or equality.

The result must include the lower bound as well as the upper bound. It uses unlabelled path copies, an attained extremal maximum, and a forbidden family consisting of the specified odd cycle. An upper bound alone, labelled copies, or a supremum over an empty class would change the theorem. The needed infrastructure consists of graph-copy counts, adjacency-matrix walk counts, finite-dimensional spectral facts, and the cited odd-cycle spectral theorem. The page states Theorem 24 "for any k,lk,lk,l"; the Lean statement and Remark 3 require k≥1k\ge1k≥1 and l≥2l\ge2l≥2, the only values for which C2k+1C_{2k+1}C2k+1​ and C2lC_{2l}C2l​ are cycles. Contributions proving the milestones or developing reusable interfaces for those objects are within scope.

Selected references

  • D. Gerbner, E. Győri, A. Methuku and M. Vizer, Generalized Turán problems for even cycles, Journal of Combinatorial Theory, Series B, 2020. arXiv:1712.07079v3; DOI:10.1016/j.jctb.2020.05.005.
  • V. Nikiforov, A spectral condition for odd cycles in graphs, Linear Algebra and its Applications 428 (2008), 1492–1498. Cited as [34] in arXiv:1712.07079v3.
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Combinatorics·Captain: mikedeng1

Generalized Turán Problems for Even Cycles 2: The Maximum Number of 2l-Cycles in a C_{2k}-Free Graph on n Vertices Is Θ(n^l), with Explicit Upper and Lower ConstantsResearch Paper

Motivation

The classical Turán problem asks for the maximum number of edges of an nnn-vertex graph that avoids a fixed subgraph. For even cycles this is one of the central open problems of extremal graph theory: Bondy and Simonovits proved that a C2kC_{2k}C2k​-free graph has O(n1+1/k)O(n^{1+1/k})O(n1+1/k) edges, and this order is known to be sharp only for k=2,3,5k=2,3,5k=2,3,5.

A generalized Turán problem replaces "edges" by "copies of a fixed graph HHH". The question of how many copies of one cycle a graph may contain while avoiding another cycle goes back to Erdős's question on pentagons in triangle-free graphs, and was taken up systematically by Alon and Shikhelman (2016). For two even cycles, Solymosi and Wong asked whether forbidding the single cycle C2kC_{2k}C2k​ (instead of all cycles up to length 2k2k2k) still forces few copies of C2lC_{2l}C2l​. Gerbner, Győri, Methuku and Vizer answer this question by determining the order of magnitude: the maximum is Θ(nl)\Theta(n^l)Θ(nl), much larger than in the girth-restricted setting. The same order of magnitude was obtained independently by Gishboliner and Shapira; the proof formalized here is different and gives explicit constants.

Timeline:

  • 1959 — Erdős and Gallai: a graph on nnn vertices with no cycle longer than kkk has at most k(n−1)/2k(n-1)/2k(n−1)/2 edges.
  • 1974 — Bondy and Simonovits: ex(n,C2k)=O(n1+1/k)\mathrm{ex}(n,C_{2k})=O(n^{1+1/k})ex(n,C2k​)=O(n1+1/k).
  • 2016 — Alon and Shikhelman introduce the systematic study of ex(n,H,F)\mathrm{ex}(n,H,F)ex(n,H,F).
  • 2017–2018 — Gerbner, Győri, Methuku and Vizer (arXiv:1712.07079), and independently Gishboliner and Shapira, show ex(n,C2l,C2k)=Θ(nl)\mathrm{ex}(n,C_{2l},C_{2k})=\Theta(n^l)ex(n,C2l​,C2k​)=Θ(nl).

Setting

All graphs are finite and simple. For graphs HHH and GGG, N(H,G)\mathcal N(H,G)N(H,G) is the number of subgraphs of GGG isomorphic to HHH (unlabelled copies). CaC_aCa​ is the cycle on a≥3a\ge3a≥3 vertices. A graph is C2kC_{2k}C2k​-free if it has no subgraph isomorphic to C2kC_{2k}C2k​ (induced or not). The generalized Turán number is

ex(n,C2l,C2k)=max⁡{N(C2l,G):G a C2k-free graph on n vertices}.\mathrm{ex}(n,C_{2l},C_{2k})=\max\{\mathcal N(C_{2l},G): G\text{ a }C_{2k}\text{-free graph on }n\text{ vertices}\}.ex(n,C2l​,C2k​)=max{N(C2l​,G):G a C2k​-free graph on n vertices}.

For a non-negative integer mmm and l≥1l\ge1l≥1, the falling factorial is (m)l=m(m−1)⋯(m−l+1)(m)_l=m(m-1)\cdots(m-l+1)(m)l​=m(m−1)⋯(m−l+1).

For distinct vertices a,ba,ba,b of GGG, the codegree f(a,b)f(a,b)f(a,b) is the number of common neighbours of aaa and bbb. Sums written ∑a≠b\sum_{a\neq b}∑a=b​ range over unordered pairs {a,b}\{a,b\}{a,b} of distinct vertices.

Two graphs appear in the lower bounds. The first is the complete bipartite graph Kk−1,n−k+1K_{k-1,n-k+1}Kk−1,n−k+1​. The second is the blow-up of C2lC_{2l}C2l​: keep lll alternate vertices x0,…,xl−1x_0,\dots,x_{l-1}x0​,…,xl−1​ of C2lC_{2l}C2l​, and replace the vertex between xix_ixi​ and xi+1x_{i+1}xi+1​ (indices mod lll) by a class SiS_iSi​ of new vertices, each joined to xix_ixi​ and xi+1x_{i+1}xi+1​. The n−ln-ln−l new vertices are split as evenly as possible among the lll classes.

Formalization targets

Goal: Theorem 10

As n→∞n\to\inftyn→∞ with kkk and lll fixed:

ex(n,C2l,C2k)≤(1+o(1))2l−2(k−1)l2lnl(l≥3, k≥2),\mathrm{ex}(n,C_{2l},C_{2k})\le(1+o(1))\frac{2^{l-2}(k-1)^l}{2l}n^l\qquad(l\ge3,\ k\ge2),ex(n,C2l​,C2k​)≤(1+o(1))2l2l−2(k−1)l​nl(l≥3, k≥2), ex(n,C2l,C2k)≥(1+o(1))(k−1)l2lnl(k>l≥2),\mathrm{ex}(n,C_{2l},C_{2k})\ge(1+o(1))\frac{(k-1)_l}{2l}n^l\qquad(k>l\ge2),ex(n,C2l​,C2k​)≥(1+o(1))2l(k−1)l​​nl(k>l≥2), ex(n,C2l,C2k)≥(1+o(1))1llnl(l>k≥3).\mathrm{ex}(n,C_{2l},C_{2k})\ge(1+o(1))\frac{1}{l^l}n^l\qquad(l>k\ge3).ex(n,C2l​,C2k​)≥(1+o(1))ll1​nl(l>k≥3).

The goal is the conjunction of the three bounds, each with its own parameter range. Note that the upper bound has the power (k−1)l(k-1)^l(k−1)l, while the second lower bound has the falling factorial (k−1)l(k-1)_l(k−1)l​.

Milestones

The lower bounds:

  • Kk−1,n−k+1K_{k-1,n-k+1}Kk−1,n−k+1​ is C2kC_{2k}C2k​-free and has exactly 12l(k−1l)(n−k+1l)l! l!\frac1{2l}\binom{k-1}{l}\binom{n-k+1}{l}l!\,l!2l1​(lk−1​)(ln−k+1​)l!l! copies of C2lC_{2l}C2l​.
  • The blow-up of C2lC_{2l}C2l​ is C2kC_{2k}C2k​-free for 3≤k<l3\le k<l3≤k<l, and its number of C2lC_{2l}C2l​'s is ∏i∣Si∣\prod_i|S_i|∏i​∣Si​∣.

The upper bound:

  • The identity 2 N(C4,G)=∑a≠b(f(a,b)2)2\,\mathcal N(C_4,G)=\sum_{a\neq b}\binom{f(a,b)}{2}2N(C4​,G)=∑a=b​(2f(a,b)​).
  • Inequality (1): 12∑a≠b(f(a,b)2)≤(1+o(1))(k−1)(k−2)4n2\tfrac12\sum_{a\ne b}\binom{f(a,b)}2\le(1+o(1))\frac{(k-1)(k-2)}4n^221​∑a=b​(2f(a,b)​)≤(1+o(1))4(k−1)(k−2)​n2 for C2kC_{2k}C2k​-free GGG, which is the upper bound of Theorem 11.
  • Theorem 2: the Erdős–Gallai bound, in its correct form.
  • Claim 1: ∑b≠af(a,b)≤(2k−2)n\sum_{b\neq a}f(a,b)\le(2k-2)n∑b=a​f(a,b)≤(2k−2)n.
  • Inequality (2): ∑a≠bf(a,b)≤(k−1)n2\sum_{a\neq b}f(a,b)\le(k-1)n^2∑a=b​f(a,b)≤(k−1)n2.
  • Inequality (3): N(C2l,G)≤14l∑∏jf(vj,vj+1)\mathcal N(C_{2l},G)\le\frac1{4l}\sum\prod_jf(v_j,v_{j+1})N(C2l​,G)≤4l1​∑∏j​f(vj​,vj+1​), together with its AM–GM refinement.
  • The final bound N(C2l,G)≤12l∑u≠vf2(u,v)(2k−2)l−2nl−2\mathcal N(C_{2l},G)\le\frac1{2l}\sum_{u\neq v}f^2(u,v)(2k-2)^{l-2}n^{l-2}N(C2l​,G)≤2l1​∑u=v​f2(u,v)(2k−2)l−2nl−2.

Significance

Theorem 10, combined with Theorem 11 on ex(n,C4,C2k)\mathrm{ex}(n,C_4,C_{2k})ex(n,C4​,C2k​), gives ex(n,C2l,C2k)=Θ(nl)\mathrm{ex}(n,C_{2l},C_{2k})=\Theta(n^l)ex(n,C2l​,C2k​)=Θ(nl) for all k,l≥2k,l\ge2k,l≥2. The one exception is the lower bound at k=2k=2k=2, which comes from the Erdős–Rényi polarity graph. This answers the Solymosi–Wong question in the negative: forbidding one even cycle allows many more copies of a shorter or longer even cycle than forbidding all short cycles does. The codegree method of the upper bound, which bounds N(C2l,G)\mathcal N(C_{2l},G)N(C2l​,G) by sums of f2f^2f2 and controls those sums through C4C_4C4​-counts and paths of length two, is reused in the paper's later results on graphs of given girth (Theorem 14).

On the formal side, none of these results is formalized in Lean. Mathlib has copyCount, cycleGraph and edge-extremal numbers, but no generalized Turán numbers, no Erdős–Gallai circumference theorem and no Bondy–Simonovits theorem. The mission produces:

  • machine-checked exact counts of cycles in complete bipartite graphs and blow-ups;
  • a formal Erdős–Gallai circumference bound;
  • the codegree inequalities that turn local degree information into cycle counts.

The paper's printed version of the Erdős–Gallai bound, (k−1)n/2(k-1)n/2(k−1)n/2, is incorrect. The mission states the bound k(n−1)/2k(n-1)/2k(n−1)/2 that Erdős and Gallai proved, which is all the argument needs.

Difficulty

The two lower bounds are explicit counts. The difficulty there is combinatorial bookkeeping in Lean. One task is matching unlabelled copies (copyCount) with the (l!)2/(2l)(l!)^2/(2l)(l!)2/(2l) Hamiltonian cycles of Kl,lK_{l,l}Kl,l​. The other is showing that the blow-up contains no cycle other than 4-cycles and full 2l2l2l-cycles, so that its 2l2l2l-cycles are exactly one choice per class.

The upper bound rests on two classical theorems that are absent from Mathlib:

  • the Erdős–Gallai circumference theorem (behind Claim 1);
  • Theorem 11's upper bound on the number of C4C_4C4​'s in a C2kC_{2k}C2k​-free graph (behind (1)), which in turn uses Bondy–Simonovits.

Without (1) one has only (f2)≤f2\binom{f}{2}\le f^2(2f​)≤f2 and no control of ∑f2\sum f^2∑f2. Without Claim 1 the chained sums in (3) are not bounded by (2k−2)n(2k-2)n(2k−2)n per step. The naive estimate, which bounds each f(vj,vj+1)f(v_j,v_{j+1})f(vj​,vj+1​) by the maximum codegree, does not work: in a C2kC_{2k}C2k​-free graph a single codegree can be of order nnn, and that only gives O(nl+1)O(n^{l+1})O(nl+1).

Formalization scope

Graphs are SimpleGraph (Fin n). Cycles are Mathlib's cycleGraph. N(H,G)\mathcal N(H,G)N(H,G) is G.copyCount H, the number of unlabelled copies (not labelledCopyCount, which is ∣Aut H∣|\mathrm{Aut}\,H|∣AutH∣ times larger). ex(n,H,CA)\mathrm{ex}(n,H,\mathcal C_A)ex(n,H,CA​) is exCyc n H A, an sSup over CA\mathcal C_ACA​-free graphs on Fin n. The underlying set is finite and contains 000, from the empty graph, so the value is an attained maximum and not a junk sSup.

Each (1+o(1))(1+o(1))(1+o(1)) is a function ε:N→R\varepsilon:\mathbb N\to\mathbb Rε:N→R with ε(n)→0\varepsilon(n)\to0ε(n)→0, depending only on kkk and lll and chosen before nnn, with the bound holding for all large nnn. Sums over a≠ba\neq ba=b are over unordered pairs (Sym2 off the diagonal). With ordered pairs every constant would change by a factor of two, and (2) would be false. Tuples of distinct vertices are embeddings Fin l ↪ V, with indices mod lll. (k−1)l(k-1)_l(k−1)l​ is Nat.descFactorial (k - 1) l.

Hypotheses added relative to the page:

  • k≥2k\ge2k≥2 in Theorem 2, which fails for forests when k≤1k\le1k≤1;
  • n≥k−1n\ge k-1n≥k−1 for Kk−1,n−k+1K_{k-1,n-k+1}Kk−1,n−k+1​;
  • n≥2ln\ge 2ln≥2l for the blow-up count.

Theorem 2 states the corrected bound 2∣E∣≤k(n−1)2|E|\le k(n-1)2∣E∣≤k(n−1).

The following do not count as the goal: a one-sided goal with only the upper or only the lower bound; a version with labelled copies; a constant or ε\varepsilonε that depends on nnn or on the graph; and a single unified parameter range.

A complete development needs:

  • exact cycle counts in Ka,bK_{a,b}Ka,b​;
  • the cycle structure of a subdivided multi-cycle;
  • the Erdős–Gallai circumference theorem;
  • Theorem 11's upper bound, and with it Bondy–Simonovits;
  • the double-counting identities for codegrees.

The Erdős–Gallai theorem, Bondy–Simonovits and the codegree identities are reusable well beyond this mission, and contributions of any of them are welcome.

Selected references

  • D. Gerbner, E. Győri, A. Methuku, M. Vizer, Generalized Turán problems for even cycles, J. Combin. Theory Ser. B 145 (2020); preprint arXiv:1712.07079v3. https://arxiv.org/abs/1712.07079 , https://doi.org/10.1016/j.jctb.2020.05.005
  • P. Erdős, T. Gallai, On maximal paths and circuits of graphs, Acta Math. Acad. Sci. Hungar. 10 (1959), 337–356. https://doi.org/10.1007/BF02024498
  • J. A. Bondy, M. Simonovits, Cycles of even length in graphs, J. Combin. Theory Ser. B 16 (1974), 97–105. https://doi.org/10.1016/0095-8956(74)90052-5
  • N. Alon, C. Shikhelman, Many T copies in H-free graphs, J. Combin. Theory Ser. B 121 (2016), 146–172. https://doi.org/10.1016/j.jctb.2016.03.004
  • L. Gishboliner, A. Shapira, A generalized Turán problem and its applications, Int. Math. Res. Not. IMRN (2020). https://arxiv.org/abs/1712.00831
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Combinatorics·Captain: mikedeng1

Generalized Turán Problems for Even Cycles 5: The Maximum Number of (2l+1)-Cycles in a Graph with No Cycle of Length 3, …, 2l or 2k Is Ω(n^{1+1/(2k+1)}) and O(n^{1+l/(l+1)})Research Paper

Why count cycles under a girth constraint?

An extremal graph question asks how many copies of one pattern can occur when other patterns are forbidden. When the counted pattern is a cycle, the answer depends strongly on which shorter cycles may occur. Excluding only an even cycle and excluding all cycles up to a given length are different constraints. The latter imposes a girth condition, meaning that nearby vertices cannot be connected by the many short alternative routes that otherwise create small cycles. For odd cycles, this makes a direct application of ordinary edge-count bounds inadequate: many graphs with relatively few edges can still arrange those edges into many copies of a fixed cycle.

Gerbner, Győri, Methuku and Vizer study generalized Turán numbers for cycles and paths. Their Theorem 17 bounds the number of copies of a particular odd cycle when every cycle of lengths 333 through 2l2l2l, and one further even cycle of length 2k2k2k, is forbidden. The lower and upper exponents differ; the theorem supplies a nontrivial interval for the possible growth rate rather than an exact asymptotic formula. The preprint, Theorem 17 is the source used here; its printed page numbers agree with its PDF pages.

Graphs, copies, and forbidden families

Write CaC_aCa​ for the simple cycle on aaa vertices. For a set AAA of integers at least 333, let CA={Ca:a∈A}\mathcal C_A=\{C_a:a\in A\}CA​={Ca​:a∈A}. A graph GGG is CA\mathcal C_ACA​-free if no member of the family occurs as a subgraph of GGG. The copies need not be induced: an extra edge between vertices of a copy does not erase that copy. When A={3,…,t}A=\{3,\ldots,t\}A={3,…,t}, write Ct\mathcal C_tCt​ for this family. Thus C2l∪{C2k}\mathcal C_{2l}\cup\{C_{2k}\}C2l​∪{C2k​} excludes every cycle of length from 333 to 2l2l2l and also excludes the single cycle C2kC_{2k}C2k​. This notation follows §1 of the paper.

For graphs HHH and GGG, let N(H,G)\mathcal N(H,G)N(H,G) be the number of unlabelled copies of HHH in GGG: subgraphs of GGG isomorphic to HHH, counted once each. The generalized Turán number is

ex⁡(n,H,CA)=max⁡{N(H,G):∣V(G)∣=n, G is CA-free}.\operatorname{ex}(n,H,\mathcal C_A) =\max\{\mathcal N(H,G): |V(G)|=n,\ G\text{ is }\mathcal C_A\text{-free}\}.ex(n,H,CA​)=max{N(H,G):∣V(G)∣=n, G is CA​-free}.

The graph with no edges is eligible for the forbidden families in this mission, and the finite vertex set makes the maximum well defined. For a vertex vvv and integer i≥0i\ge0i≥0, Ni(v)N_i(v)Ni​(v) is the set of vertices whose graph distance from vvv is exactly iii. Vertices in another connected component lie in no Ni(v)N_i(v)Ni​(v) with finite iii.

The lower-bound milestone also uses a hypergraph, a set of hyperedges that are subsets of a common vertex set. It is rrr-uniform when every hyperedge has exactly rrr vertices. A Berge cycle of length jjj alternates jjj distinct vertices and jjj distinct hyperedges; consecutive vertices lie in their connecting hyperedge. Hypergraph girth at least sss means that no Berge cycle has length less than sss. These definitions are used in Theorem 29 and §6.2.

Formalization targets

Fix integers k>l≥2k>l\ge2k>l≥2. The goal is the two-sided statement of Theorem 17, p. 7:

ex⁡(n,C2l+1,C2l∪{C2k})=Ω(n1+1/(2k+1)),\operatorname{ex}(n,C_{2l+1},\mathcal C_{2l}\cup\{C_{2k}\}) =\Omega\bigl(n^{1+1/(2k+1)}\bigr),ex(n,C2l+1​,C2l​∪{C2k​})=Ω(n1+1/(2k+1)), ex⁡(n,C2l+1,C2l∪{C2k})=O(n1+l/(l+1)).\operatorname{ex}(n,C_{2l+1},\mathcal C_{2l}\cup\{C_{2k}\}) =O\bigl(n^{1+l/(l+1)}\bigr).ex(n,C2l+1​,C2l​∪{C2k​})=O(n1+l/(l+1)).

Both estimates concern n→∞n\to\inftyn→∞ with kkk and lll fixed. The paper does not claim that either exponent is optimal for every pair (k,l)(k,l)(k,l), and these formulas should not be read as an equality of growth rates.

The milestones record the source statements needed on the two sides. Theorem 29 gives rrr-uniform hypergraphs of prescribed Berge girth with at least n1+1/sn^{1+1/s}n1+1/s hyperedges. The lower-bound paragraph of §6.2 turns such hypergraphs into graphs with many C2l+1C_{2l+1}C2l+1​ copies. Lemma 27 bounds the edges of a graph under a path-restricting partition condition. The local layer identity and Claim 18 control edges in the distance layers Nl(v)N_l(v)Nl​(v) and Nl+1(v)N_{l+1}(v)Nl+1​(v). The final milestone is the low-cycle-vertex conclusion from the paper's deletion passage on p. 28.

What the result provides

The upper exponent 1+l/(l+1)1+l/(l+1)1+l/(l+1) is strictly below 222, so the theorem rules out quadratic growth for this generalized Turán number under the stated forbidden family. The lower exponent 1+1/(2k+1)1+1/(2k+1)1+1/(2k+1) shows that the number of permitted odd cycles nevertheless grows faster than linearly. Together they locate the problem between two explicit power laws. These statements belong to a wider set of cycle-count results in the paper, where changing the forbidden family or the parity of its extra cycle changes the resulting bounds; see Theorems 17 and 18.

The paper proves these bounds. The work here is to state its objects and claims so that Lean can check future proofs: the maximum over admissible graphs, unlabelled cycle counts, Berge-cycle girth, and graph distance layers. The present proposal supplies proof targets; their Lean declarations have placeholders for proofs. The finite graph and hypergraph definitions can also serve later formalizations of generalized Turán and girth questions. The exact growth rate between the two exponents is left open by this theorem.

Where the proof is difficult

Counting cycles from an ordinary upper bound on the number of edges loses the relation between a cycle and its position around a vertex. The no-short-cycle condition constrains individual distance layers, but a layer can still contain edges that close a C2l+1C_{2l+1}C2l+1​. Controlling those edges uniformly across all graphs and vertices is the central issue in the upper bound. A bound with a constant chosen after seeing the graph would say little about the asymptotic maximum; Claim 18 instead requires a constant depending only on kkk and lll. The lower side has a different requirement: the construction must preserve both the abundance of odd cycles and every exclusion in C2l∪{C2k}\mathcal C_{2l}\cup\{C_{2k}\}C2l​∪{C2k​}. The precise assertions are in §6.2, pp. 27–28.

Formalization scope

Graphs on nnn vertices are represented as simple graphs on Fin⁡(n)\operatorname{Fin}(n)Fin(n), and CaC_aCa​ is Mathlib's cycle graph. A copy is a subgraph isomorphic to CaC_aCa​, counted without labels. The extremal number is the supremum of its finite, nonempty set of attainable natural-number counts; in this setting that supremum is the paper's maximum. Distance layers use extended graph distance, which assigns unreachable pairs infinity. The family of forbidden lengths is exactly {3,…,2l}∪{2k}\{3,\ldots,2l\}\cup\{2k\}{3,…,2l}∪{2k}; it does not omit any short cycle.

The hypergraph representation is a finite set of distinct finite vertex sets. Berge-cycle existence requires injective lists of vertices and hyperedges, and girth at least sss is expressed by absence of lengths 222 through s−1s-1s−1 rather than a numerical infimum with an exceptional value on acyclic hypergraphs. Theorem 29's threshold is chosen after r,sr,sr,s and before nnn. Claim 18's constant is chosen after k,lk,lk,l and before the graph or vertex. The upper and lower asymptotic bounds are both present, with real exponents; neither is replaced by a one-sided or purely existential statement.

Lemma 27 is given with the explicit m∣V(G)∣m|V(G)|m∣V(G)∣ edge bound from its proof, uniform in the number of partition classes. The final deletion milestone uses an existential constant D(k,l)D(k,l)D(k,l) because the page mixes several powers of its intermediate constant ccc; this is recorded in the accompanying audit notes. A complete proof development needs Mathlib's simple-graph copies, paths, graph metric, finite-set counting, and asymptotic notation, plus the hypergraph interface defined for this mission. Proofs of the goal and any of these milestones are welcome.

Selected references

  • D. Gerbner, E. Győri, A. Methuku, and M. Vizer, Generalized Turán problems for even cycles, Journal of Combinatorial Theory, Series B, 2020. arXiv:1712.07079v3; DOI: 10.1016/j.jctb.2020.05.005.
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Cycle Lengths in Expanding Graphs 3: For 0 < β < 1/20, Every β-Graph on n Vertices Has a Cycle of Every Length from O(1/log(1/β))·log n to (1−O(β))·nResearch Paper

Motivation

Which cycle lengths a graph must contain is a classical question of extremal graph theory. A graph on nnn vertices is pancyclic if it has cycles of every length from 333 to nnn; Bondy's meta-conjecture (Bondy 1971) and a long line of results ask which density or expansion conditions force pancyclicity or, more modestly, force the set of cycle lengths to contain a long interval. For sparse graphs, expansion is the natural hypothesis: random graphs and explicit expanders are sparse, yet very well connected.

Friedman and Krivelevich, Cycle lengths in expanding graphs (arXiv:1912.11011v2; Combinatorica 41, 2021), study two expansion conditions. For the usual vertex expanders they show that cycle lengths are numerous and well spread, but they also note that an expander need not contain any interval of cycle lengths of non-trivial length. Section 4 of the paper therefore introduces a different, two-set expansion condition, the β-graph, and shows that it does force a complete interval of cycle lengths of length linear in nnn. This mission formalizes that result, Theorem 3.

Timeline.

  • Friedman and Pippenger (1987) proved that graphs with strong vertex expansion contain every bounded-degree tree of a given size.
  • Haxell (2001) generalized that embedding theorem to expansion conditions on sets up to twice a threshold; the form used here is the one stated by Balogh, Csaba, Pei and Samotij (2010).
  • Hefetz, Krivelevich and Szabó (2009) proved that for large nnn and β=O(log⁡log⁡n/log⁡n)\beta = O(\log\log n/\log n)β=O(loglogn/logn), every β-graph on nnn vertices has a cycle of every length ℓ\ellℓ with 8βnlog⁡nlog⁡log⁡n≤ℓ≤(1−3β)n\frac{8\beta n\log n}{\log\log n}\le\ell\le(1-3\beta)nloglogn8βnlogn​≤ℓ≤(1−3β)n, and conjectured that the lower end can be improved to clog⁡n/log⁡(1/β)c\log n/\log(1/\beta)clogn/log(1/β).
  • Friedman and Krivelevich (2019 preprint, 2021 journal) proved this for every fixed β<1/20\beta<1/20β<1/20, with the upper end (1−O(β))n(1-O(\beta))n(1−O(β))n (Theorem 3).

Setting

All graphs are finite and simple. For a graph G=(V,E)G=(V,E)G=(V,E) and a vertex set U⊆VU\subseteq VU⊆V, the external neighborhood of UUU is

NG(U)={v∈V∖U:v has a neighbor in U}.N_G(U)=\{v\in V\setminus U : v\text{ has a neighbor in }U\}.NG​(U)={v∈V∖U:v has a neighbor in U}.

Let GGG have nnn vertices and let β>0\beta>0β>0. GGG is a β-graph if every two disjoint vertex sets A,B⊆VA,B\subseteq VA,B⊆V with ∣A∣,∣B∣≥βn|A|,|B|\ge\beta n∣A∣,∣B∣≥βn are joined by an edge. For n≥1n\ge1n≥1, the complete graph is a β-graph for every β>0\beta>0β>0; when βn≤1\beta n\le 1βn≤1, only the complete graph is. No graph on zero vertices is a β-graph.

The set of cycle lengths of GGG is L(G)={ℓ:G contains a cycle of length ℓ}L(G)=\{\ell : G\text{ contains a cycle of length }\ell\}L(G)={ℓ:G contains a cycle of length ℓ}. A cycle of length ℓ\ellℓ has ℓ≥3\ell\ge3ℓ≥3 distinct vertices.

The proof works with the tree Tk,t,pT_{k,t,p}Tk,t,p​: two complete kkk-ary trees of depth ttt (every internal vertex has kkk children, every leaf is at distance ttt from the root) whose roots are joined by a path of length ppp. A graph contains a copy of a tree TTT if there is an injective map from the vertices of TTT to those of GGG sending edges to edges.

Logarithms are to base 222.

Formalization targets

Goal: Theorem 3 (p. 3, restated p. 15)

For every 0<β<1200<\beta<\frac{1}{20}0<β<201​ there exist positive constants b1=O(1log⁡(1/β))b_1=O\bigl(\frac{1}{\log(1/\beta)}\bigr)b1​=O(log(1/β)1​) and b2=O(β)b_2=O(\beta)b2​=O(β) such that every β-graph GGG on nnn vertices satisfies

{ℓ∈Z:b1log⁡n≤ℓ≤(1−b2)n}⊆L(G).\{\ell\in\mathbb Z : b_1\log n\le\ell\le(1-b_2)n\}\subseteq L(G).{ℓ∈Z:b1​logn≤ℓ≤(1−b2​)n}⊆L(G).

The goal fixes the shape of the bound, not its constants: the proof's choice is b1=25/log⁡(1/β)b_1=25/\log(1/\beta)b1​=25/log(1/β), and b2b_2b2​ is a multiple of β\betaβ. As β→0\beta\to0β→0 the interval widens towards [O(log⁡n/log⁡(1/β)), (1−O(β))n][O(\log n/\log(1/\beta)),\,(1-O(\beta))n][O(logn/log(1/β)),(1−O(β))n]. The upper end (1−O(β))n(1-O(\beta))n(1−O(β))n is tight up to the constant: the disjoint union of Kn+1−βnK_{n+1-\beta n}Kn+1−βn​ with βn−1\beta n-1βn−1 isolated vertices is a β-graph.

Milestones

In the order of the proof (pp. 15–18):

  1. Lemma 4.1 (p. 15): every β-graph contains an induced subgraph HHH on at least (1−β)n(1-\beta)n(1−β)n vertices with ∣NH(U)∣≥1−3β2β∣U∣|N_H(U)|\ge\frac{1-3\beta}{2\beta}|U|∣NH​(U)∣≥2β1−3β​∣U∣ for all ∣U∣≤βn|U|\le\beta n∣U∣≤βn.
  2. Large sets in HHH (p. 16): ∣NH(U)∣>(1−4β)n|N_H(U)|>(1-4\beta)n∣NH​(U)∣>(1−4β)n whenever βn≤∣U∣≤2βn\beta n\le|U|\le2\beta nβn≤∣U∣≤2βn.
  3. Theorem 4.1 (Haxell, p. 16): expansion d∣U∣+1d|U|+1d∣U∣+1 on sets of size at most mmm and d∣U∣+Md|U|+Md∣U∣+M on sets of size in (m,2m](m,2m](m,2m] forces every tree with MMM vertices and maximum degree at most ddd.
  4. Cycle from a tree (p. 16): a copy of Tk,t,ℓ−2t−1T_{k,t,\ell-2t-1}Tk,t,ℓ−2t−1​ with k≥2k\ge2k≥2 and kt≥βnk^t\ge\beta nkt≥βn in a β-graph yields a cycle of length ℓ\ellℓ.
  5. Condition 1 of Theorem 4.1 for d=⌊1/(4β)⌋+1d=\lfloor1/(4\beta)\rfloor+1d=⌊1/(4β)⌋+1 (p. 17) and for d=3d=3d=3 (p. 18).
  6. The arithmetic of p. 17: 2⌈log⁡(βn)⌉+1<(12−10β)n2\lceil\log(\beta n)\rceil+1<(\frac12-10\beta)n2⌈log(βn)⌉+1<(21​−10β)n and 25log⁡(1/β)log⁡n≥2⌈log⁡k(βn)⌉+1\frac{25}{\log(1/\beta)}\log n\ge2\lceil\log_k(\beta n)\rceil+1log(1/β)25​logn≥2⌈logk​(βn)⌉+1 for large nnn.

Significance

The result. Theorem 3 proves the conjecture of Hefetz, Krivelevich and Szabó in a stronger form: β may be any constant below 1/201/201/20 instead of tending to zero with nnn, and the lower end O(log⁡n/log⁡(1/β))O(\log n/\log(1/\beta))O(logn/log(1/β)) is of optimal order. Since spectral expanders satisfy the β-graph condition (by the expander mixing lemma an (n,d,λ)(n,d,\lambda)(n,d,λ)-graph with d/∣λ∣≥1/β2d/|\lambda|\ge1/\beta^2d/∣λ∣≥1/β2 is a β-graph), the theorem gives long intervals of cycle lengths in pseudo-random graphs. The method, embedding two large trees joined by a path and closing the cycle across their leaf sets, is a template for producing cycles of prescribed length in expanding graphs.

Formalizing it. The result is proved on paper. As far as the platform's records show, none of it is formalized: Mathlib has trees, walks, cycles and graph copies, but no tree-embedding theorem for expanders and no result on cycle lengths of this kind. The main piece of new infrastructure is Haxell's theorem (milestone 3), which the paper cites without proof. Its proof (in Haxell's paper and in Balogh–Csaba–Pei–Samotij) is a few pages of inductive embedding with a Hall-type invariant. A formal proof of Theorem 4.1 would be reusable for any embedding argument for bounded-degree trees, for example in Ramsey and size-Ramsey problems for trees and paths. The printed proof also has a rounding gap (below), so the formal proof will have to settle a slightly different argument from the printed one.

Difficulty

The β-graph condition only speaks about pairs of large sets, while cycles are built edge by edge, so short and long cycles both need a mechanism. The obvious approach, finding a long path and then shortcutting it, does not control which lengths occur. The proof instead needs a robust embedding theorem for trees with about 2βn2\beta n2βn leaves inside a subgraph with good expansion of small sets, and that is Theorem 4.1, a deep result.

A second difficulty is quantitative. In the printed verification of claims (1) and (2) on p. 17, a complete kkk-ary tree of depth t=⌈log⁡k(βn)⌉t=\lceil\log_k(\beta n)\rceilt=⌈logk​(βn)⌉ is counted as having about kβn/(k−1)k\beta n/(k-1)kβn/(k−1) vertices, as if kt=βnk^t=\beta nkt=βn exactly. In fact ktk^tkt can be almost kβnk\beta nkβn, and for claim (2) the honest count only covers ℓ≤(1−18β)n\ell\le(1-18\beta)nℓ≤(1−18β)n rather than (1−14β)n(1-14\beta)n(1−14β)n. For k=⌊1/(4β)⌋k=\lfloor1/(4\beta)\rfloork=⌊1/(4β)⌋ the gap is larger. Theorem 3 is unaffected, because its constants are only O(⋅)O(\cdot)O(⋅) and pruning the trees to ⌈βn⌉\lceil\beta n\rceil⌈βn⌉ leaves repairs the count. But a solver following the printed proof will need to repair that step. The two claims are therefore not posed as milestones.

Formalization scope

Graphs on nnn vertices are SimpleGraph (Fin n). Vertex sets are Set with Set.ncard, and every inequality involving β, a logarithm or nnn is stated in ℝ after casting. Logarithms are Real.logb 2. Cycles are Mathlib's Walk.IsCycle, so every cycle length is at least 333. "Contains a copy" is SimpleGraph.IsContained (injective and edge-preserving, not induced). The subgraph G′=HG'=HG′=H of Lemma 4.1 is the induced subgraph on a set SSS, with NH(U)=NG(U)∩SN_H(U)=N_G(U)\cap SNH​(U)=NG​(U)∩S. The tree Tk,t,pT_{k,t,p}Tk,t,p​ is defined on words of length at most ttt over {0,…,k−1}\{0,\dots,k-1\}{0,…,k−1} (two tagged copies) plus p−1p-1p−1 path vertices.

Conventions of the goal:

  • O(⋅)O(\cdot)O(⋅): one absolute constant K>0K>0K>0 is quantified before β, with b1≤K/log⁡2(1/β)b_1\le K/\log_2(1/\beta)b1​≤K/log2​(1/β) and b2≤Kβb_2\le K\betab2​≤Kβ. A constant chosen after β would make the O(⋅)O(\cdot)O(⋅) claims empty, so that encoding is ruled out. Since β<1/20\beta<1/20β<1/20, log⁡2(1/β)>4\log_2(1/\beta)>4log2​(1/β)>4 and the division is by a positive number.
  • Threshold: the statement holds for n≥n0n\ge n_0n≥n0​ with n0n_0n0​ depending on β only. This is a disclosed, necessary addition: without it the interval contains ℓ=2\ell=2ℓ=2 at n=3n=3n=3 for small β, and no graph has a cycle of length 222.
  • Every integer in the interval is a cycle length, not some integer.
  • A cycle is a genuine cycle, not a closed walk.

Infrastructure needed: Haxell's theorem (reusable well beyond this mission), counting leaves and vertices of complete kkk-ary trees, and turning an embedded Tk,t,pT_{k,t,p}Tk,t,p​ plus one edge into a Walk.IsCycle. Contributions to any milestone are welcome, in particular a proof of Theorem 4.1 and a repaired version of claims (1) and (2) with pruned trees.

Selected references

  • L. Friedman and M. Krivelevich, Cycle lengths in expanding graphs, Combinatorica 41 (2021). arXiv:1912.11011v2. https://arxiv.org/abs/1912.11011v2, https://doi.org/10.1007/s00493-020-4434-0
  • P. E. Haxell, Tree embeddings, Journal of Graph Theory 36 (2001), 121–130. https://doi.org/10.1002/1097-0118(200103)36:3<121::AID-JGT1000>3.0.CO;2-U
  • J. Friedman and N. Pippenger, Expanding graphs contain all small trees, Combinatorica 7 (1987), 71–76. https://doi.org/10.1007/BF02579202
  • J. Balogh, B. Csaba, M. Pei and W. Samotij, Large bounded degree trees in expanding graphs, Electronic Journal of Combinatorics 17 (2010), R6. https://doi.org/10.37236/279
  • J. A. Bondy, Pancyclic graphs I, Journal of Combinatorial Theory, Ser. B 11 (1971), 80–84. https://doi.org/10.1016/0095-8956(71)90016-5
  • D. Hefetz, M. Krivelevich and T. Szabó, Hamilton cycles in highly connected and expanding graphs, Combinatorica 29 (2009), 547–568. https://doi.org/10.1007/s00493-009-2362-1
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Finding Cliques in Social Networks: A New Distribution-Free Model 3: For Every c ≥ 2 and n ≥ c There Is a c-Closed Graph on n Vertices with Ω(c^(−3/2)·2^(c/2)·n^(3/2)) Maximal CliquesResearch Paper

Motivation

Social networks exhibit triadic closure: two people with many common friends tend to be friends themselves. Fox, Roughgarden, Seshadhri, Wei and Wein (arXiv:1804.07431, SIAM J. Comput. 2020) turn this empirical regularity into a deterministic graph class, the ccc-closed graphs, and ask how hard a classical NP-hard problem, enumerating maximal cliques, becomes on it. In general graphs the number of maximal cliques can be exponential: Moon and Moser showed that the maximum over nnn-vertex graphs is 3n/33^{n/3}3n/3 (Moon–Moser 1965). The paper proves that on ccc-closed graphs the count is polynomial in nnn for every fixed ccc, with upper bounds of order 3(c−1)/3n23^{(c-1)/3} n^23(c−1)/3n2 and 4(c+4)(c−1)/2n2−21−c4^{(c+4)(c-1)/2} n^{2-2^{1-c}}4(c+4)(c−1)/2n2−21−c.

This mission formalizes the matching lower bound of the paper (Section 4): the exponent of nnn cannot be pushed below 3/23/23/2, and the dependence on ccc must be exponential.

Prior work: Eschen, Hoàng, Spinrad and Sritharan (2011) studied the 222-closed graphs under a different name (graphs without an induced C4C_4C4​ or diamond) and showed that they have O(n3/2)O(n^{3/2})O(n3/2) maximal cliques, with a matching lower bound from incidence graphs of finite projective planes. Fox et al. (2018) extended the lower bound to every ccc by blowing up a graph of girth 555.

Setting

All graphs are finite, simple and undirected. For vertices u,vu, vu,v of a graph G=(V,E)G=(V,E)G=(V,E), N(u)∩N(v)N(u)\cap N(v)N(u)∩N(v) is the set of their common neighbours.

  • A graph is ccc-closed (Definition 1.1) if any two distinct vertices with at least ccc common neighbours are adjacent. Every ccc-closed graph is c′c'c′-closed for c′≥cc' \ge cc′≥c; 111-closed graphs are exactly disjoint unions of cliques.
  • A maximal clique is a set of pairwise adjacent vertices to which no further vertex of GGG can be added. #MC(G)\#\mathrm{MC}(G)#MC(G) denotes the number of maximal cliques of GGG.
  • The girth of HHH is the length of its shortest cycle (∞\infty∞ for a forest). Girth at least 555 means: no triangle and no 444-cycle.
  • The blow-up H(h)H^{(h)}H(h) of a graph HHH replaces each vertex xxx by a set UxU_xUx​ of hhh vertices (x,0),…,(x,h−1)(x,0),\dots,(x,h-1)(x,0),…,(x,h−1) and joins
(x,i)∼(y,j)  ⟺  i≠j  and  (x=y  or  xy∈E(H)).(x,i)\sim(y,j)\iff i\neq j\ \text{ and }\ \bigl(x=y\ \text{ or }\ xy\in E(H)\bigr).(x,i)∼(y,j)⟺i=j  and  (x=y  or  xy∈E(H)).

Each UxU_xUx​ is a clique; for an edge xyxyxy of HHH the bipartite graph between UxU_xUx​ and UyU_yUy​ is Kh,hK_{h,h}Kh,h​ minus a perfect matching; non-adjacent x,yx,yx,y give no edges between UxU_xUx​ and UyU_yUy​. The paper takes h=c/2h = c/2h=c/2.

Formalization targets

Goal: Theorem 4.1

There is an absolute constant C>0C>0C>0 such that for all integers c≥2c\ge 2c≥2 and n≥cn\ge cn≥c there is a ccc-closed graph GGG on nnn vertices with

#MC(G) ≥ C c−3/2 2c/2 n3/2.\#\mathrm{MC}(G)\ \ge\ C\,c^{-3/2}\,2^{c/2}\,n^{3/2}.#MC(G) ≥ Cc−3/22c/2n3/2.

The constant is absolute; the dependence on ccc is explicit. This is the paper's Theorem 4.1 (restatement of Theorem 1.7: "for any positive integer ccc, there are ccc-closed graphs with nnn vertices and Ω(c−3/22c/2n3/2)\Omega(c^{-3/2}2^{c/2}n^{3/2})Ω(c−3/22c/2n3/2) maximal cliques") with the two hypotheses c≥2c \ge 2c≥2 and n≥cn \ge cn≥c made explicit; see Formalization scope.

Milestones

  1. Girth-five graphs with many edges (cited from [29] on p. 12): there is C>0C>0C>0 such that for every v≥2v\ge 2v≥2 some graph on vvv vertices has girth at least 555 and at least Cv3/2C v^{3/2}Cv3/2 edges.
  2. §1.4, p. 5: a graph of girth at least 555 is 222-closed and each of its edges is a maximal clique, so #MC(H)≥∣E(H)∣\#\mathrm{MC}(H)\ge |E(H)|#MC(H)≥∣E(H)∣.
  3. Claim 4.2: for h≥1h\ge 1h≥1 and HHH of girth at least 555, the blow-up H(h)H^{(h)}H(h) is 2h2h2h-closed.
  4. Claim 4.3 (corrected count): there is an absolute C>0C>0C>0 such that for every k≥1k\ge1k≥1 and h≥2h\ge2h≥2 one can choose a girth-at-least-555 graph HHH on 2k2k2k vertices whose blow-up satisfies
#MC(H(h)) ≥ C (2h)−3/2 2h (2kh)3/2.\#\mathrm{MC}\bigl(H^{(h)}\bigr)\ \ge\ C\,(2h)^{-3/2}\,2^h\,(2kh)^{3/2}.#MC(H(h)) ≥ C(2h)−3/22h(2kh)3/2.

Significance

The result. Together with the paper's upper bounds, Theorem 4.1 brackets the maximum number of maximal cliques in a ccc-closed nnn-vertex graph between n3/2n^{3/2}n3/2 and n2−21−cn^{2-2^{1-c}}n2−21−c, up to factors depending on ccc. It shows that the exponential dependence on ccc in the upper bounds is unavoidable, so fixed-parameter tractability in ccc is the right notion, and that no bound of the form f(c) naf(c)\,n^{a}f(c)na with a<3/2a<3/2a<3/2 holds. Determining the true exponent of nnn is stated as an open problem in Section 5 of the paper.

Formalizing it. The result is proved on paper; no machine-checked proof is known. A formal development produces a verified lower-bound construction for maximal cliques, a reusable blow-up operation with its clique structure, and, through milestone 1, a formal proof of the classical Ω(v3/2)\Omega(v^{3/2})Ω(v3/2) lower bound for C4C_4C4​-free graphs, which Mathlib does not yet contain.

Difficulty

The combinatorial core (Claims 4.2 and 4.3) is a finite case analysis, but the paper's own count is not correct as printed: of the 2c/22^{c/2}2c/2 cliques obtained from an edge xyxyxy, two are UxU_xUx​ and UyU_yUy​, which every edge at xxx or yyy produces again, so they are not distinct across edges. The corrected per-edge count is 2c/2−22^{c/2}-22c/2−2, and for c=2c=2c=2 the blow-up is edgeless, so small ccc must be handled by HHH itself.

The main difficulty is milestone 1. Graphs of girth 555 with Ω(v3/2)\Omega(v^{3/2})Ω(v3/2) edges for every vvv require an algebraic construction (polarity graphs of projective planes over Fq\mathbb F_qFq​, or incidence graphs, which have girth 666) together with a density-of-primes argument to pass from v≈q2v \approx q^2v≈q2 to arbitrary vvv. Random graphs do not suffice: deletion arguments give only Ω(v4/3)\Omega(v^{4/3})Ω(v4/3) edges. Finally, Theorem 4.1 is stated for all n≥cn \ge cn≥c and all c≥2c \ge 2c≥2, while the construction works for even ccc and c∣nc \mid nc∣n; the paper notes that the reduction loses only an absolute constant factor, but a complete proof has to carry it out uniformly in ccc and nnn, including the range where nnn is close to ccc.

Formalization scope

  • Graphs on nnn vertices are SimpleGraph (Fin n); the blow-up lives on V × Fin h. Common neighbours are counted with Set.ncard, and "at least ccc" is c ≤, not c <.
  • #MC(G)\#\mathrm{MC}(G)#MC(G) counts finite vertex sets that are maximal (Mathlib's Maximal) among cliques; it does not count all cliques, nor cliques of a fixed size.
  • Girth is Mathlib's egirth (valued in N∪{∞}\mathbb N\cup\{\infty\}N∪{∞}): "girth ≥5\ge 5≥5" is 5 ≤ H.egirth, which admits forests. The ℕ-valued girth, which is 000 for forests, is not used. Edge counts are H.edgeSet.ncard.
  • The perfect matching between UxU_xUx​ and UyU_yUy​ is fixed as (x,i)↔(y,i)(x,i)\leftrightarrow(y,i)(x,i)↔(y,i) for every edge of HHH, one admissible choice in the paper's construction (which allows any perfect matching).
  • Powers c−3/2c^{-3/2}c−3/2, 2c/22^{c/2}2c/2, n3/2n^{3/2}n3/2 are real powers.
  • Added hypotheses, disclosed. The goal adds c≥2c\ge 2c≥2 and n≥cn\ge cn≥c to the printed "for any positive integer ccc". Without c≥2c\ge 2c≥2 the statement is false, since a 111-closed graph has at most nnn maximal cliques. Without n≥cn \ge cn≥c it is false, since Moon–Moser caps every nnn-vertex graph at 3n/33^{n/3}3n/3 maximal cliques, which is below c−3/22c/2n3/2c^{-3/2}2^{c/2}n^{3/2}c−3/22c/2n3/2 when nnn is small compared with ccc. The construction itself assumes that nnn is a positive multiple of ccc. Claim 4.3 is restricted to h≥2h\ge 2h≥2: for h=1h=1h=1 the blow-up is edgeless. Its proof counts (2h−2)∣E(H)∣(2^h-2)|E(H)|(2h−2)∣E(H)∣ distinct mixed cliques, correcting the paper's printed per-edge count while retaining its Ω\OmegaΩ conclusion. The reductions to even ccc and c∣nc\mid nc∣n are part of the proof, not hypotheses.
  • Ruled out. The constant CCC is chosen before ccc and nnn; a statement with CCC depending on ccc or nnn would make the factor c−3/22c/2c^{-3/2}2^{c/2}c−3/22c/2 meaningless. Milestone 1 requires both the girth condition and the edge bound; either alone is trivial (the complete graph, or the empty graph). Counting all cliques instead of maximal ones, or reading "at least ccc" as "more than ccc", changes the theorem.
  • The shared CClosedGraphs.Peeling.Setting supplies IsCClosed and the maximal-clique counts; CClosedGraphs.LowerBound.Setting defines the blow-up. Contributions of a general Mathlib-style theory of C4C_4C4​-free extremal graphs (polarity graphs, Turán number of C4C_4C4​) are especially welcome and reusable beyond this mission.

Selected references

  • J. Fox, T. Roughgarden, C. Seshadhri, F. Wei, N. Wein, Finding cliques in social networks: a new distribution-free model, arXiv:1804.07431v1, 2018; SIAM J. Comput. 49(2), 2020. https://arxiv.org/abs/1804.07431, https://doi.org/10.1137/18M1210459
  • E. M. Eschen, C. T. Hoàng, J. P. Spinrad, R. Sritharan, On graphs without a C4C_4C4​ or a diamond, Discrete Appl. Math. 159(7):581–587, 2011. https://arxiv.org/abs/0909.4719
  • Z. Füredi, M. Simonovits, The history of degenerate (bipartite) extremal graph problems, in Erdős Centennial, Bolyai Soc. Math. Stud. 25, Springer, 2013, pp. 169–264. https://arxiv.org/abs/1306.5167
  • J. W. Moon, L. Moser, On cliques in graphs, Israel J. Math. 3(1):23–28, 1965. https://doi.org/10.1007/BF02760024
  • W. G. Brown, On graphs that do not contain a Thomsen graph, Canad. Math. Bull. 9:281–285, 1966. https://doi.org/10.4153/CMB-1966-036-2
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Combinatorics·Captain: mikedeng1

Generalized Turán Problems for Even Cycles 1: A C_{2k}-Free Graph on n Vertices Has at Most (1+o(1))(k−1)(k−2)n²/4 Four-Cycles, and This Is AttainedResearch Paper

Motivation

The Turán problem asks for ex(n,F)\mathrm{ex}(n,F)ex(n,F), the largest number of edges in an nnn-vertex graph with no copy of a fixed graph FFF. For even cycles the answer is still not known in general: Bondy and Simonovits proved ex(n,C2k)=O(n1+1/k)\mathrm{ex}(n,C_{2k})=O(n^{1+1/k})ex(n,C2k​)=O(n1+1/k) in 1974, and the order of magnitude is known to be sharp only for k=2,3,5k=2,3,5k=2,3,5.

The generalized Turán problem replaces edges by copies of another graph HHH: how many copies of HHH can an nnn-vertex graph contain if it has no copy of FFF? Its systematic study was started by Alon and Shikhelman (arXiv:1506.03807, 2016). Gerbner, Győri, Methuku and Vizer (arXiv:1712.07079, J. Combin. Theory Ser. B, 2020) treat the case where both HHH and the forbidden graph are cycles. This mission formalizes their Theorem 11, the case H=C4H=C_4H=C4​, F=C2kF=C_{2k}F=C2k​, which is the one case of the paper where the asymptotics, including the leading constant, are determined exactly for every kkk.

Timeline:

  • 1974, Bondy and Simonovits: every C2kC_{2k}C2k​-free graph on nnn vertices has O(n1+1/k)O(n^{1+1/k})O(n1+1/k) edges.
  • 2016, Alon and Shikhelman: general framework for ex(n,H,F)\mathrm{ex}(n,H,F)ex(n,H,F); first bounds for cycles.
  • 2017 (arXiv v1; v3 December 2018), Gerbner, Győri, Methuku and Vizer: ex(n,C4,C2k)=(1+o(1))(k−1)(k−2)4n2\mathrm{ex}(n,C_4,C_{2k})=(1+o(1))\frac{(k-1)(k-2)}4n^2ex(n,C4​,C2k​)=(1+o(1))4(k−1)(k−2)​n2, and the order of magnitude of ex(n,C2l,C2k)\mathrm{ex}(n,C_{2l},C_{2k})ex(n,C2l​,C2k​).

Setting

All graphs are finite and simple. For graphs HHH and GGG, N(H,G)\mathcal N(H,G)N(H,G) is the number of copies of HHH in GGG: the number of subgraphs of GGG isomorphic to HHH (unlabelled copies). CaC_aCa​ is the cycle on aaa vertices. A graph is C2kC_{2k}C2k​-free if it has no subgraph isomorphic to C2kC_{2k}C2k​ (not necessarily induced). The generalized Turán number is

ex(n,H,C2k)=max⁡{N(H,G):G a C2k-free graph on n vertices}.\mathrm{ex}(n,H,C_{2k})=\max\{\mathcal N(H,G): G \text{ a } C_{2k}\text{-free graph on } n \text{ vertices}\}.ex(n,H,C2k​)=max{N(H,G):G a C2k​-free graph on n vertices}.

In Lean, vertices are Fin n, CaC_aCa​ is Mathlib's cycleGraph a, N(H,G)\mathcal N(H,G)N(H,G) is G.copyCount H, the C2kC_{2k}C2k​-free condition is CycleFree {2 * k} G, and ex(n,H,CA)\mathrm{ex}(n,H,\mathcal C_A)ex(n,H,CA​) is exCyc n H A.

The proof works with the following objects, all defined in the mission's definitions file. The codegree of two vertices is their number of common neighbours. A pair of distinct vertices is fat if it has at least kkk common neighbours. A copy of C4C_4C4​ with vertices a,x,b,ya,x,b,ya,x,b,y in cyclic order has the two opposite pairs {a,b}\{a,b\}{a,b} and {x,y}\{x,y\}{x,y}; it is a fat C4C_4C4​ if both opposite pairs are fat. A greedy run processes the fat C4C_4C4​'s in an arbitrary order and picks from each one of its four edges that has been picked the fewest times so far; the multiplicity m(e)m(e)m(e) of an edge is the number of times it is picked. Ka,n−aK_{a,n-a}Ka,n−a​ is the complete bipartite graph with classes of sizes aaa and n−an-an−a (bipGraph n a).

Formalization targets

Goal: Theorem 11

For every k≥2k\ge 2k≥2,

ex(n,C4,C2k)=(1+o(1)) (k−1)(k−2)4 n2(n→∞).\mathrm{ex}(n,C_4,C_{2k})=(1+o(1))\,\frac{(k-1)(k-2)}{4}\,n^2 \qquad (n\to\infty).ex(n,C4​,C2k​)=(1+o(1))4(k−1)(k−2)​n2(n→∞).

The o(1)o(1)o(1) is a sequence depending only on kkk. At k=2k=2k=2 the statement says ex(n,C4,C4)=0\mathrm{ex}(n,C_4,C_4)=0ex(n,C4​,C4​)=0.

Milestones

  1. Lower bound. Kk−1,n−k+1K_{k-1,n-k+1}Kk−1,n−k+1​ is C2kC_{2k}C2k​-free and has exactly (k−12)(n−k+12)\binom{k-1}2\binom{n-k+1}2(2k−1​)(2n−k+1​) copies of C4C_4C4​.
  2. Non-fat four-cycles. Every C2kC_{2k}C2k​-free graph on nnn vertices has at most (k−12)(n2)\binom{k-1}2\binom n2(2k−1​)(2n​) non-fat copies of C4C_4C4​.
  3. Kk,kK_{k,k}Kk,k​-freeness. In a C2kC_{2k}C2k​-free graph any kkk vertices have at most k−1k-1k−1 common neighbours.
  4. Claim 2. Under that property, if some edge has multiplicity at least 2(k−2)k2(2kk)2(k-2)k^2\binom{2k}k2(k−2)k2(k2k​), then for every 2≤l≤k2\le l\le k2≤l≤k some C2lC_{2l}C2l​ contains an edge of multiplicity at least 2(k−l)k2(2kk)2(k-l)k^2\binom{2k}k2(k−l)k2(k2k​).
  5. Fat four-cycles. A C2kC_{2k}C2k​-free graph has at most 2(k−2)k2(2kk) ∣E(G)∣2(k-2)k^2\binom{2k}k\,|E(G)|2(k−2)k2(k2k​)∣E(G)∣ fat copies of C4C_4C4​.
  6. Theorem 1 (Bondy–Simonovits). ex(n,C2k)≤Ck n1+1/k\mathrm{ex}(n,C_{2k})\le C_k\,n^{1+1/k}ex(n,C2k​)≤Ck​n1+1/k.
  7. Upper bound. Every C2kC_{2k}C2k​-free graph on nnn vertices has at most (1+o(1))(k−1)(k−2)4n2(1+o(1))\frac{(k-1)(k-2)}4n^2(1+o(1))4(k−1)(k−2)​n2 copies of C4C_4C4​.

Significance

The result. Theorem 11 gives the exact leading constant for the maximum number of four-cycles in a graph without a 2k2k2k-cycle. The extremal number of edges of such a graph is only known up to a constant factor, and only for k∈{2,3,5}k\in\{2,3,5\}k∈{2,3,5} is even its order known; counting four-cycles instead of edges turns the problem into one with a clean answer, attained by the unbalanced complete bipartite graph Kk−1,n−k+1K_{k-1,n-k+1}Kk−1,n−k+1​. The same upper bound on four-cycles is used again in the paper's Theorem 10, on the number of C2lC_{2l}C2l​'s in C2kC_{2k}C2k​-free graphs.

Formalizing it. The theorem is proved in the paper; it has not been formalized. A complete development needs counting of four-cycles through codegrees, the greedy multiplicity bookkeeping of §4.1, an argument producing long cycles in graphs with many fat four-cycles, and the Bondy–Simonovits theorem, which is itself not in Mathlib. The milestones isolate each of these; Theorem 1 is a stand-alone classical theorem of independent value.

Difficulty

The non-fat four-cycles give the main term directly. The difficulty is that fat four-cycles can a priori be numerous: a dense bipartite graph has Θ(n4)\Theta(n^4)Θ(n4) of them. One must show that C2kC_{2k}C2k​-freeness forces them down to O(n1+1/k)O(n^{1+1/k})O(n1+1/k), which is o(n2)o(n^2)o(n2). The argument that bounds the non-fat ones does not transfer: a fat pair can lie in arbitrarily many four-cycles as an opposite pair, and nothing a priori bounds the number of fat pairs by less than (n2)\binom n2(2n​). The bound must instead come from the global absence of C2kC_{2k}C2k​, which is a statement about long cycles, while the objects being counted are short ones; Claim 2 is where the two meet, and its bookkeeping (new vertices must avoid the current cycle, and the four-cycles that fail to extend it must be outnumbered) is the core of the proof. The upper bound then still rests on the Bondy–Simonovits theorem, whose proof is a substantial argument of its own.

Formalization scope

Graphs are SimpleGraph (Fin n) (or a general finite vertex type for the per-graph milestones). Copies are unlabelled (copyCount); a labelled count would be 8 times larger for C4C_4C4​. ex\mathrm{ex}ex is an sSup over naturals; with forbidden lengths at least 3 the set is nonempty and finite, so it is a true maximum, which a sorry-free sanity file in the workspace checks. Landau notation is in n→∞n\to\inftyn→∞ with kkk fixed first: (1+o(1))(1+o(1))(1+o(1)) is a real sequence ε(n)→0\varepsilon(n)\to 0ε(n)→0 chosen before nnn and the graph, with the equality holding for all large nnn, and the O(⋅)O(\cdot)O(⋅) of Theorem 1 is an explicit constant chosen before nnn and the graph. The constant (k−1)(k−2)4\frac{(k-1)(k-2)}44(k−1)(k−2)​ is computed in R\mathbb RR.

Statements that are trivially true are ruled out: the goal states both the upper and the lower bound as an equality, the maximum is not a junk sSup, copies are unlabelled, constants and o(1)o(1)o(1) functions are fixed before the graph, and the forbidden family is exactly {C2k}\{C_{2k}\}{C2k​}.

Two hypotheses differ from the page, both in Claim 2. The paper's standing graph is C2kC_{2k}C2k​-free, which would make Claim 2 vacuous (its conclusion at l=kl=kl=k is a C2kC_{2k}C2k​); the milestone assumes instead the property its proof uses, that any kkk vertices have at most k−1k-1k−1 common neighbours. It also assumes that the edge eee is picked at least once, which follows from the multiplicity bound when k≥3k\ge3k≥3 and is needed for the base case at k=2k=2k=2. The fat-four-cycle milestone states the explicit bound 2(k−2)k2(2kk)∣E(G)∣2(k-2)k^2\binom{2k}k|E(G)|2(k−2)k2(k2k​)∣E(G)∣ that the proof establishes, rather than the O(n1+1/k)O(n^{1+1/k})O(n1+1/k) obtained after Theorem 1.

Contributions welcome: proofs of any milestone; a formal proof of the Bondy–Simonovits theorem, reusable well beyond this mission; general lemmas on counting four-cycles via codegrees and on copies in complete bipartite graphs.

Selected references

  • D. Gerbner, E. Győri, A. Methuku, M. Vizer, Generalized Turán problems for even cycles, J. Combin. Theory Ser. B (2020); arXiv:1712.07079v3. https://arxiv.org/abs/1712.07079, https://doi.org/10.1016/j.jctb.2020.05.005
  • J. A. Bondy, M. Simonovits, Cycles of even length in graphs, J. Combin. Theory Ser. B 16 (1974) 97–105. https://doi.org/10.1016/0095-8956(74)90052-5
  • N. Alon, C. Shikhelman, Many T copies in H-free graphs, J. Combin. Theory Ser. B 121 (2016) 146–172. https://arxiv.org/abs/1506.03807
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Combinatorics·Captain: mikedeng1

Cycle Lengths in Expanding Graphs 1: Every α-Expander on n Vertices Has a Cycle of Length in [ℓ, ℓ+O(1/α)] for Every Integer ℓ from O(1/α)·log n up to 2^(−O(log(1/α)/α))·nResearch Paper

Motivation

Which cycle lengths a graph must contain is a long-standing theme of extremal graph theory, studied for graphs of given minimum degree, chromatic number or density. A natural class in which to ask it is that of expanders: graphs in which every not-too-large vertex set has many neighbours outside it (Hoory, Linial and Wigderson). It is known that an α\alphaα-expander on nnn vertices has a cycle of length Ωα(n)\Omega_\alpha(n)Ωα​(n) and a cycle of length Oα(log⁡n)O_\alpha(\log n)Oα​(logn) (Krivelevich's survey, Corollary 3.2 and Theorem 7.4, arXiv:1812.11562); the question is what happens between these extremes.

One cannot hope for a full interval of cycle lengths: the complete bipartite graph Kαn/2,(1−α/2)nK_{\alpha n/2,(1-\alpha/2)n}Kαn/2,(1−α/2)n​ is an α\alphaα-expander without odd cycles, and subdividing every edge of a bounded-degree expander mmm times gives an Ω(α/m)\Omega(\alpha/m)Ω(α/m)-expander all of whose cycle lengths are divisible by m+1m+1m+1. L. Friedman and M. Krivelevich (Cycle lengths in expanding graphs, Combinatorica 41 (2021); arXiv:1912.11011v2) show that this is the only obstruction: the cycle lengths of an α\alphaα-expander are well spread, in the sense that every window of O(1/α)O(1/\alpha)O(1/α) consecutive integers between logarithmic and linear length contains one. This mission formalizes that theorem (Theorem 1 of the paper) together with the lemmas of its §2.

Setting

Let G=(V,E)G=(V,E)G=(V,E) be a finite simple graph on nnn vertices. For U⊆VU\subseteq VU⊆V, the external neighbourhood is NG(U)={v∈V∖U:v has a neighbour in U}N_G(U)=\{v\in V\setminus U : v\text{ has a neighbour in }U\}NG​(U)={v∈V∖U:v has a neighbour in U}.

  • For reals k>0k>0k>0, α>0\alpha>0α>0, GGG is a (k,α)(k,\alpha)(k,α)-expander if ∣NG(U)∣≥α∣U∣|N_G(U)|\ge\alpha|U|∣NG​(U)∣≥α∣U∣ for every U⊆VU\subseteq VU⊆V with ∣U∣≤k|U|\le k∣U∣≤k.
  • GGG is an α\alphaα-expander if ∣NG(U)∣≥α∣U∣|N_G(U)|\ge\alpha|U|∣NG​(U)∣≥α∣U∣ for every UUU with ∣U∣≤⌈n/2⌉|U|\le\lceil n/2\rceil∣U∣≤⌈n/2⌉.
  • A cycle of length ℓ≥3\ell\ge3ℓ≥3 is a closed walk with ℓ\ellℓ edges and no repeated vertex; L(G)L(G)L(G) is the set of lengths of cycles of GGG.
  • BG(v,r)B_G(v,r)BG​(v,r) is the ball of radius rrr: the vertices at distance at most rrr from vvv.
  • For a tree T⊆GT\subseteq GT⊆G rooted at v0v_0v0​, the level of a vertex is one plus its distance from v0v_0v0​ in TTT, so L1={v0}L_1=\{v_0\}L1​={v0​}, and T[j1,j2]=⋃i=j1j2LiT_{[j_1,j_2]}=\bigcup_{i=j_1}^{j_2}L_iT[j1​,j2​]​=⋃i=j1​j2​​Li​.
  • Contracting the parts V1,…,VrV_1,\dots,V_rV1​,…,Vr​ of a partition of VVV gives the graph on {1,…,r}\{1,\dots,r\}{1,…,r} in which i≠ji\ne ji=j are adjacent when some edge of GGG joins ViV_iVi​ and VjV_jVj​.

All logarithms are base 222. In Lean these objects are extNbhd, IsKAlphaExpander, IsAlphaExpander, cycleLengths, ball, contract, level and levelSet, in the namespace CycleLengthsExp.WellSpread.

Formalization targets

Goal: Theorem 1 (p. 3)

For every 0<α≤10<\alpha\le10<α≤1 there are positive constants A=O(1/α)A=O(1/\alpha)A=O(1/α), a1=O(1/α)a_1=O(1/\alpha)a1​=O(1/α) and a2=2−O(log⁡(1/α)/α)a_2=2^{-O(\log(1/\alpha)/\alpha)}a2​=2−O(log(1/α)/α) such that for all large nnn, every α\alphaα-expander GGG on nnn vertices satisfies

∀ ℓ∈Z∩[a1log⁡n, a2n]∃ L∈L(G): ℓ≤L≤ℓ+A.\forall\,\ell\in\mathbb Z\cap[a_1\log n,\ a_2n]\quad\exists\,L\in L(G):\ \ell\le L\le\ell+A .∀ℓ∈Z∩[a1​logn, a2​n]∃L∈L(G): ℓ≤L≤ℓ+A.

The goal fixes only the shape of the dependence on α\alphaα, not the constants, so an improvement of the constants does not invalidate it.

Milestones (§2, in the order the proof uses them)

  1. Ball growth (p. 4): in a (k,α)(k,\alpha)(k,α)-expander, ∣BG(v,r)∣≥min⁡{k,(1+α)r}|B_G(v,r)|\ge\min\{k,(1+\alpha)^r\}∣BG​(v,r)∣≥min{k,(1+α)r}.
  2. Lemma 2.1 (p. 4): a component of a (δn,α)(\delta n,\alpha)(δn,α)-expander (0<δ<10<\delta<10<δ<1, 0<α≤10<\alpha\le10<α≤1) has diameter at most 7δαlog⁡n\frac7{\delta\alpha}\log nδα7​logn.
  3. Lemma 2.2 (p. 5): removing ϵn\epsilon nϵn vertices, 0<ϵ≤α2k/(8n)0<\epsilon\le\alpha^2k/(8n)0<ϵ≤α2k/(8n), from a (k,α)(k,\alpha)(k,α)-expander leaves an induced (k,α/2)(k,\alpha/2)(k,α/2)-expander on (1−3ϵ/α)n(1-3\epsilon/\alpha)n(1−3ϵ/α)n vertices.
  4. Lemma 2.3 (p. 5): an α\alphaα-expander has tα/(1+α)t\alpha/(1+\alpha)tα/(1+α) vertex-disjoint paths between any two sets of size at least ttt.
  5. Lemma 2.4 (p. 6): contracting parts of size at most CCC yields a (k/C,α/C)(k/C,\alpha/C)(k/C,α/C)-expander on r≥n/Cr\ge n/Cr≥n/C vertices.
  6. Lemma 2.5 (p. 6): if every kkk-set has at least ℓ\ellℓ external neighbours, a path of length ℓ\ellℓ starts at every vertex whose component has at least kkk vertices.
  7. Lemma 2.6 (p. 6): a set WWW with ∣W∣>n/2|W|>n/2∣W∣>n/2 and ∣NG(W)∣≤αϵn|N_G(W)|\le\alpha\epsilon n∣NG​(W)∣≤αϵn contains an induced ((12−2ϵ)n,α/2)((\tfrac12-2\epsilon)n,\alpha/2)((21​−2ϵ)n,α/2)-expander on more than (12−2ϵ)n(\tfrac12-2\epsilon)n(21​−2ϵ)n vertices.
  8. Lemma 2.7 (p. 7): a tree rooted at any v0v_0v0​ with levels k0≤C0log⁡nk_0\le C_0\log nk0​≤C0​logn, k1−k0≤C1k_1-k_0\le C_1k1​−k0​≤C1​, k2−k1≤C2k_2-k_1\le C_2k2​−k1​≤C2​, bounded degree Δ\DeltaΔ on T[k1,k2]T_{[k_1,k_2]}T[k1​,k2​]​, and an (n/10,α/5)(n/10,\alpha/5)(n/10,α/5)-expander on at least n/10n/10n/10 vertices inside T[k1,k2]T_{[k_1,k_2]}T[k1​,k2​]​.

Significance

The result. Theorem 1 says that, up to an additive error of order 1/α1/\alpha1/α, an α\alphaα-expander contains every cycle length from O(log⁡n/α)O(\log n/\alpha)O(logn/α) up to a constant fraction of nnn. The additive error is optimal in its dependence on α\alphaα by the subdivision example, and the logarithmic lower end is necessary because expanders can have logarithmic girth. It implies that ∣L(G)∣|L(G)|∣L(G)∣ is linear in nnn for every α\alphaα-expander; the companion mission of this series (Theorem 2 of the paper) improves the dependence on α\alphaα in that count. The lemmas of §2, especially the robust sub-expander lemmas 2.2 and 2.6, the disjoint-paths lemma 2.3 and the depth-first-search lemma 2.5, are general-purpose statements about vertex expanders that do not depend on the cycle-length question.

Formalizing it. The theorem is proved on paper; none of it is machine-checked. Mathlib has walks, paths, cycles, distances and induced subgraphs, but no vertex expansion, no Menger theorem, no depth-first-search path lemma and no breadth-first-tree construction. This mission produces formal statements of all of these at the level of generality the paper uses, and a complete development would supply reusable lemmas about vertex expanders on finite simple graphs.

Difficulty

The obvious argument fails at the step of hitting a prescribed length. Expansion easily gives long paths and long cycles, but controlling the length of a cycle to within an additive O(1/α)O(1/\alpha)O(1/α) requires a structure with many cycles whose lengths differ by small, controlled amounts. The paper builds it from two breadth-first trees joined by many short disjoint paths, a long path routed through bounded-size subtrees, and a pigeonhole selection that avoids spoiling too many subtrees; the explicit constants of Lemma 2.7 then have to be propagated through Theorem 1. Lemma 2.3 additionally needs Menger's theorem, which is not in Mathlib, and Lemma 2.5 is a depth-first-search argument whose formal invariant is not short.

Formalization scope

  • Graphs are SimpleGraph (Fin n); the ball-growth milestone uses any finite vertex type. Sizes are Set.ncard; inequalities involving α\alphaα, ϵ\epsilonϵ, δ\deltaδ or logarithms are stated in ℝ. ⌈n/2⌉\lceil n/2\rceil⌈n/2⌉ is (n + 1) / 2. Logarithms are Real.logb 2.
  • O(⋅)O(\cdot)O(⋅) constants: one absolute K>0K>0K>0 quantified before α\alphaα. In Theorem 1, A≤K/αA\le K/\alphaA≤K/α, a1≤K/αa_1\le K/\alphaa1​≤K/α, a2≥2−Klog⁡2(2/α)/αa_2\ge2^{-K\log_2(2/\alpha)/\alpha}a2​≥2−Klog2​(2/α)/α; in Lemma 2.7, Δ,C1≤K/α5\Delta,C_1\le K/\alpha^5Δ,C1​≤K/α5 and C0,C2≤K/αC_0,C_2\le K/\alphaC0​,C2​≤K/α. Inside the OOO, log⁡(1/α)\log(1/\alpha)log(1/α) is read as log⁡2(2/α)\log_2(2/\alpha)log2​(2/α), which agrees up to a constant for α≤1/2\alpha\le1/2α≤1/2 and avoids the value 000 at α=1\alpha=1α=1.
  • Thresholds: Theorem 1's "for large enough nnn" is n≥n0n\ge n_0n≥n0​ with n0n_0n0​ depending on α\alphaα only. Lemma 2.7 is also stated for n≥n0(α)n\ge n_0(\alpha)n≥n0​(α): an addition, disclosed in the item, because the paper's proof suppresses rounding and needs nnn large against 1/α1/\alpha1/α, and Theorem 1 uses the lemma only for large nnn.
  • Induced subgraphs: "G[U]G[U]G[U] is an expander" and "contains an expander" use G.induce U, the edges of GGG, never the edges of a tree.
  • Lemma 2.1 is stated in its "In particular" form, as a bound on G.dist u v for u,vu,vu,v in one component. The first inequality of the lemma is false as printed (the path PnP_nPn​ with k=1k=1k=1) and is not posed. Lemma 2.7, item 4, is posed with α/5\alpha/5α/5 as printed, although its proof gives α/2\alpha/2α/2.
  • Trivializing readings are ruled out: the OOO-constants are bounded by one KKK fixed before α\alphaα; cycles are Walk.IsCycle, never closed walks; Lemma 2.1 never uses G.diam, which is 000 for a disconnected graph; and no statement divides by log⁡(1/α)\log(1/\alpha)log(1/α).
  • Welcome contributions: Menger's theorem for vertex sets in finite simple graphs; the depth-first-search long-path lemma; breadth-first trees with level bookkeeping; and proofs of the milestones in any order.

Selected references

  • L. Friedman, M. Krivelevich, Cycle lengths in expanding graphs, Combinatorica 41 (2021). arXiv:1912.11011v2, doi:10.1007/s00493-020-4434-0
  • M. Krivelevich, Expanders — how to find them, and what to find in them, Surveys in Combinatorics 2019, LMS Lecture Note Series 456, 115–142. arXiv:1812.11562
  • S. Hoory, N. Linial, A. Wigderson, Expander graphs and their applications, Bulletin of the AMS 43 (2006), 439–561. doi:10.1090/S0273-0979-06-01126-8
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Finding Cliques in Social Networks: A New Distribution-Free Model 1: Every Weakly c-Closed Graph on n Vertices Has at Most 3^((c−1)/3)·n² Maximal CliquesResearch Paper

Motivation

Social networks exhibit triadic closure: two people with a common friend are more likely to be friends themselves than two people chosen at random, and the more common friends they have, the more likely they are to be connected. The principle goes back to sociology (Granovetter 1973) and is documented on large network data sets (see the Enron e-mail graph in Figure 1 of Fox, Roughgarden, Seshadhri, Wei and Wein).

A common computational task on such networks is to list all maximal cliques, the groups of mutually connected vertices that cannot be enlarged. On an arbitrary graph this is hopeless in the worst case: Moon and Moser showed in 1965 that an nnn-vertex graph can have 3n/33^{n/3}3n/3 maximal cliques, and that no graph has more (Moon and Moser 1965). Yet clique enumeration on real social networks runs quickly. Fox, Roughgarden, Seshadhri, Wei and Wein proposed a distribution-free graph class, defined by a coarse form of triadic closure, on which the worst case is provably polynomial. This mission formalizes their first bound, for the class of weakly ccc-closed graphs (Theorem 2.2 of the paper, also stated as Theorem 1.5), together with the peeling argument that proves it.

Setting

All graphs are finite, simple and undirected. For a graph GGG with vertex set VVV and a vertex vvv, N(v)N(v)N(v) is the set of neighbours of vvv; for W⊆VW \subseteq VW⊆V, G[W]G[W]G[W] is the subgraph induced by WWW. The common neighbours of two vertices u,vu, vu,v are the vertices of N(u)∩N(v)N(u) \cap N(v)N(u)∩N(v).

  • ccc-closed graph (Definition 1.1). For a positive integer ccc, GGG is ccc-closed if every two distinct vertices with at least ccc common neighbours are adjacent. A 1-closed graph is a disjoint union of cliques; every graph on nnn vertices is (n−1)(n-1)(n−1)-closed.
  • Bad pair (Definition 1.2). Given ccc, a bad pair of a graph is a pair of distinct, non-adjacent vertices with at least ccc common neighbours. A graph is ccc-closed exactly when it has no bad pair.
  • Weakly ccc-closed graph (Definition 1.3). A graph on nnn vertices is weakly ccc-closed if its vertices can be ordered v1,…,vnv_1, \dots, v_nv1​,…,vn​ so that for every iii, the vertex viv_ivi​ is in no bad pair of the induced subgraph G[{vi,vi+1,…,vn}]G[\{v_i, v_{i+1}, \dots, v_n\}]G[{vi​,vi+1​,…,vn​}]. Common neighbours are counted inside that subgraph.
  • Maximal clique. A clique is a set of pairwise adjacent vertices. It is maximal if no other vertex can be added to it. Write mc(G)\mathrm{mc}(G)mc(G) for the number of maximal cliques of GGG and mc(G[W])\mathrm{mc}(G[W])mc(G[W]) for that of an induced subgraph. The paper writes F(n,c)F(n,c)F(n,c) for the largest value of mc(G)\mathrm{mc}(G)mc(G) over ccc-closed graphs GGG on nnn vertices.

Every ccc-closed graph is weakly ccc-closed, but not conversely. The paper's example: a clique on kkk vertices with one edge {u,v}\{u,v\}{u,v} removed is not ccc-closed for any c<k−2c < k-2c<k−2, but it is weakly 1-closed (order uuu and vvv last).

In the Lean development these objects are IsCClosed, IsBadPairIn, IsWeaklyCClosed, maxCliquesIn, numMaxCliquesIn and numMaxCliques, in the namespace CClosedGraphs.Peeling.

Formalization targets

Goal: Theorem 2.2

For all positive integers ccc and nnn, every weakly ccc-closed graph GGG on nnn vertices satisfies

mc(G)≤3(c−1)/3 n2.\mathrm{mc}(G) \le 3^{(c-1)/3}\, n^2 .mc(G)≤3(c−1)/3n2.

Milestones

The milestones follow the proof of Theorem 2.1 in §2.1 and the remark that extends it to Theorem 2.2. Fix a graph, an induced subgraph G[W]G[W]G[W] and a vertex v∈Wv \in Wv∈W. A maximal clique KKK of G[W]G[W]G[W] is of type 1 if v∉Kv \notin Kv∈/K, of type 2 if v∈Kv \in Kv∈K and K∖{v}K \setminus \{v\}K∖{v} is maximal in G[W∖{v}]G[W \setminus \{v\}]G[W∖{v}], and of type 3 otherwise.

  1. Moon–Moser bound (cited as [45], p. 7): mc(G[W])≤3∣W∣/3\mathrm{mc}(G[W]) \le 3^{|W|/3}mc(G[W])≤3∣W∣/3.
  2. Types 1 and 2 (p. 6): there are at most mc(G[W∖{v}])\mathrm{mc}(G[W \setminus \{v\}])mc(G[W∖{v}]) of them.
  3. Type-3 structure (p. 7, properties A–C): for a type-3 clique KKK there is u∈W∖(N(v)∪{v})u \in W \setminus (N(v) \cup \{v\})u∈W∖(N(v)∪{v}) such that K∖{v}K \setminus \{v\}K∖{v} is a maximal clique of G[W∩N(v)∩N(u)]G[W \cap N(v) \cap N(u)]G[W∩N(v)∩N(u)].
  4. Inequality (1) (p. 7): the number of type-3 cliques is at most ∑u∈W∖(N(v)∪{v})mc(G[W∩N(u)∩N(v)])\sum_{u \in W \setminus (N(v)\cup\{v\})} \mathrm{mc}(G[W \cap N(u) \cap N(v)])∑u∈W∖(N(v)∪{v})​mc(G[W∩N(u)∩N(v)]).
  5. One-vertex recursion (pp. 7–8): if vvv is in no bad pair of G[W]G[W]G[W], then mc(G[W])≤mc(G[W∖{v}])+3(c−1)/3∣W∣\mathrm{mc}(G[W]) \le \mathrm{mc}(G[W \setminus \{v\}]) + 3^{(c-1)/3}|W|mc(G[W])≤mc(G[W∖{v}])+3(c−1)/3∣W∣.
  6. Theorem 2.1 (p. 6): every ccc-closed graph on n≥1n \ge 1n≥1 vertices has mc(G)≤3(c−1)/3n2\mathrm{mc}(G) \le 3^{(c-1)/3} n^2mc(G)≤3(c−1)/3n2.

Significance

For fixed ccc, the theorem makes the number of maximal cliques of a weakly ccc-closed graph polynomial in nnn, against 3n/33^{n/3}3n/3 for general graphs. Since all maximal cliques can be listed in polynomial time per clique, this makes maximal-clique enumeration fixed-parameter tractable in ccc on the class. The paper's Theorem 2.3 turns the proof into an enumeration algorithm. Weak ccc-closure is the version that matters in practice: Table 1 of the paper reports social networks with thousands of vertices that are weakly ccc-closed for modest ccc while being ccc-closed only for much larger ccc. The bound is the starting point of the paper's sharper Theorem 3.1 (4(c+4)(c−1)/2n2−21−c4^{(c+4)(c-1)/2} n^{2-2^{1-c}}4(c+4)(c−1)/2n2−21−c) and its lower bound Theorem 4.1, which are separate missions of this series.

The result is proved in the paper, and the proof is short. No machine-checked proof of it or of the Moon–Moser bound exists in Mathlib at the pinned revision or on Prove2Me. This mission produces both, together with a reusable vocabulary for counting maximal cliques of induced subgraphs. Proofs of the sharp Moon–Moser values, or of the sharper ∣W∣−1|W|-1∣W∣−1 in the recursion, are welcome as further contributions but are not milestones.

Difficulty

The counting argument is elementary. The work lies in two places. First, the Moon–Moser bound 3k/33^{k/3}3k/3 is not in Mathlib and needs its own proof, and its exponential bound must be handled with real powers. Second, the type decomposition must be handled exactly: a type-3 clique KKK is charged to a pair (u,K∖{v})(u, K \setminus \{v\})(u,K∖{v}), and the charge has to be injective while uuu ranges over all vertices outside N(v)∪{v}N(v) \cup \{v\}N(v)∪{v}. The degenerate cases are real. When vvv is isolated in G[W]G[W]G[W] and W≠{v}W \ne \{v\}W={v}, the clique {v}\{v\}{v} is of type 3 and K∖{v}=∅K \setminus \{v\} = \varnothingK∖{v}=∅. The peeling for weakly ccc-closed graphs must also follow the given vertex ordering, with closure checked in the shrinking induced subgraphs rather than in GGG.

Formalization scope

  • Graphs on nnn vertices are SimpleGraph (Fin n). Auxiliary statements are generic over a finite type with decidable equality.
  • The weakly closed ordering is a permutation σ\sigmaσ of Fin n with vi=σ(i)v_i = \sigma(i)vi​=σ(i). The set {vi,…,vn}\{v_i, \dots, v_n\}{vi​,…,vn​} is {x:i≤σ−1(x)}\{x : i \le \sigma^{-1}(x)\}{x:i≤σ−1(x)}.
  • Common-neighbour counts are Set.ncard, which is exact on a finite type. "At least ccc" is c≤∣⋅∣c \le |\cdot|c≤∣⋅∣, not c<∣⋅∣c < |\cdot|c<∣⋅∣.
  • Maximal cliques of G[W]G[W]G[W] are counted as finite vertex sets K⊆WK \subseteq WK⊆W of the ambient type that are maximal among cliques contained in WWW. Removing vvv from the current graph G[W]G[W]G[W] is G[W∖{v}]G[W \setminus \{v\}]G[W∖{v}], and G[N(v)∩N(u)]G[N(v) \cap N(u)]G[N(v)∩N(u)] is G[W∩N(v)∩N(u)]G[W \cap N(v) \cap N(u)]G[W∩N(v)∩N(u)].
  • All bounds are real numbers: 3(c−1)/33^{(c-1)/3}3(c−1)/3 and 3∣W∣/33^{|W|/3}3∣W∣/3 are real powers.
  • The hypotheses c≥1c \ge 1c≥1 and n≥1n \ge 1n≥1 are on the page. The second is needed, since the graph with no vertices has one maximal clique, the empty one.
  • No hypothesis is added relative to the paper. In property C, "u∉N(v)u \notin N(v)u∈/N(v)" is read as u∉N(v)∪{v}u \notin N(v) \cup \{v\}u∈/N(v)∪{v}, as in Figure 2 and in (1).
  • The paper's F(⋅,c)F(\cdot, c)F(⋅,c) is a maximum over graphs. The milestones use the actual count of the relevant induced subgraph instead, which is at most FFF; the goals bound the count for every graph in the class, which is the same content.

Three trivializing readings are ruled out. The count is of maximal cliques, not of all cliques or of cliques of a given size. A bad pair needs at least ccc common neighbours, not more than ccc. In the weakly closed definition, common neighbours are counted inside {vi,…,vn}\{v_i, \dots, v_n\}{vi​,…,vn​}, not in GGG; counting them in GGG would make the hypothesis stronger and the theorem weaker.

A complete development needs a Moon–Moser lemma for induced subgraphs, the injections behind milestones 2 and 4, and a peeling induction over a vertex ordering. The first two are reusable for any maximal-clique counting argument. The induced-subgraph counts numMaxCliquesIn are also used by the later missions of this series, which repeat the definitions in their own namespaces.

Selected references

  • J. Fox, T. Roughgarden, C. Seshadhri, F. Wei, N. Wein, Finding cliques in social networks: a new distribution-free model, arXiv:1804.07431v1, 2018; SIAM Journal on Computing 49(2), 2020. https://arxiv.org/abs/1804.07431v1, https://doi.org/10.1137/18M1210459
  • J. W. Moon, L. Moser, On cliques in graphs, Israel Journal of Mathematics 3, 23–28, 1965. https://doi.org/10.1007/BF02760024
  • M. S. Granovetter, The strength of weak ties, American Journal of Sociology 78(6), 1360–1380, 1973. https://doi.org/10.1086/225469
  • E. Tomita, A. Tanaka, H. Takahashi, The worst-case time complexity for generating all maximal cliques and computational experiments, Theoretical Computer Science 363(1), 28–42, 2006. https://doi.org/10.1016/j.tcs.2006.06.015
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Exact Solution of the Hypergraph Turán Problem for k-Uniform Linear Paths: For k ≥ 4 and Large n, the Turán Numbers of P_{2t+1} and P_{2t+2} and Their Unique Extremal FamiliesResearch Paper

Motivation

Hypergraph Turán problems ask how many edges a kkk-uniform hypergraph on nnn vertices can have without containing a fixed sub-hypergraph. For graphs (k=2k=2k=2) the theory is well developed; for k≥3k\ge 3k≥3 exact answers are rare and are known mostly for small, dense configurations such as the Fano plane or expanded triangles. Paths are the natural sparse test case. For graphs, Erdős and Gallai (1959) showed that a graph on nnn vertices without a path of length ℓ\ellℓ has at most 12(ℓ−1)n\frac12(\ell-1)n21​(ℓ−1)n edges. For hypergraphs there are several notions of path: Berge paths (Győri, Katona and Lemons, 2010), loose paths (Mubayi and Verstraëte, 2007, asymptotic bounds) and the most restrictive notion, linear paths. The case of two edges was solved by Frankl (1977), and the case ℓ<k\ell<kℓ<k asymptotically by Frankl and Füredi (1987).

Füredi, Jiang and Seiver (arXiv:1108.1247v1, published in Combinatorica 34 (2014), doi:10.1007/s00493-014-2838-4) determined the Turán number of every kkk-uniform linear path exactly, for k≥4k\ge4k≥4 and nnn large, together with the unique extremal families. It is one of the few exact hypergraph Turán results for an infinite family of forbidden hypergraphs.

Setting

The vertex set is [n]={1,…,n}[n]=\{1,\dots,n\}[n]={1,…,n}. A family F\mathcal FF is a set of subsets of [n][n][n]; it is kkk-uniform if F⊆([n]k)\mathcal F\subseteq\binom{[n]}{k}F⊆(k[n]​), the set of all kkk-element subsets.

A linear path of length ℓ\ellℓ is a sequence of distinct sets F1,…,FℓF_1,\dots,F_\ellF1​,…,Fℓ​ such that

∣Fi∩Fi+1∣=1  for each i,Fi∩Fj=∅  whenever ∣i−j∣>1.|F_i\cap F_{i+1}|=1\ \text{ for each } i,\qquad F_i\cap F_j=\emptyset\ \text{ whenever } |i-j|>1.∣Fi​∩Fi+1​∣=1  for each i,Fi​∩Fj​=∅  whenever ∣i−j∣>1.

The kkk-uniform linear path of length ℓ\ellℓ is written Pℓ(k)\mathbb P^{(k)}_\ellPℓ(k)​; a family contains it if some ℓ\ellℓ of its members form a linear path. The Turán number exk(n,Pℓ(k))\mathbf{ex}_k(n,\mathbb P^{(k)}_\ell)exk​(n,Pℓ(k)​) is the maximum size of a kkk-uniform family on [n][n][n] that does not contain Pℓ(k)\mathbb P^{(k)}_\ellPℓ(k)​.

Two counting functions describe the answer:

f(n,k,t)=∑i=1t(n−ik−1),g(n,k,t)=f(n,k,t)+(n−t−2k−2).f(n,k,t)=\sum_{i=1}^{t}\binom{n-i}{k-1},\qquad g(n,k,t)=f(n,k,t)+\binom{n-t-2}{k-2}.f(n,k,t)=i=1∑t​(k−1n−i​),g(n,k,t)=f(n,k,t)+(k−2n−t−2​).

For a set S⊆[n]S\subseteq[n]S⊆[n] of ttt elements, f(n,k,t)f(n,k,t)f(n,k,t) is the number of kkk-sets meeting SSS (the star family of SSS), and g(n,k,t)g(n,k,t)g(n,k,t) adds the kkk-sets of [n]∖S[n]\setminus S[n]∖S that contain two fixed vertices u,vu,vu,v (the even family).

The proof is phrased in the language of the delta-system method: sss-stars (sets pairwise meeting in a common kernel), the kernel degree deg⁡F∗(W)\deg^*_{\mathcal F}(W)degF∗​(W) (the largest sss for which an sss-star with kernel WWW exists), (k,s)(k,s)(k,s)-homogeneous families (k-partite families whose intersections follow a single intersection-closed pattern J\mathcal JJ of subsets of [k][k][k], each intersection being the kernel of an sss-star), the rank of J\mathcal JJ, the kernel graph of F\mathcal FF, and the kkk-blowup H(k)H^{(k)}H(k) of a graph HHH.

Formalization targets

Goal: Theorem 2.4

For every k≥4k\ge 4k≥4 and t≥1t\ge1t≥1 there is n0n_0n0​ such that for all n≥n0n\ge n_0n≥n0​:

exk(n,P2t+1(k))=f(n,k,t),exk(n,P2t+2(k))=g(n,k,t),\mathbf{ex}_k(n,\mathbb P^{(k)}_{2t+1})=f(n,k,t),\qquad \mathbf{ex}_k(n,\mathbb P^{(k)}_{2t+2})=g(n,k,t),exk​(n,P2t+1(k)​)=f(n,k,t),exk​(n,P2t+2(k)​)=g(n,k,t),

and in each case a kkk-uniform, path-free family of that size is exactly the star family of some ttt-set SSS (odd case), respectively the even family of some ttt-set SSS and two distinct vertices u,v∉Su,v\notin Su,v∈/S (even case).

Milestones

In the order the proof uses them:

  1. the star and even families are path-free and have fff and ggg members (lower bound);
  2. Lemma 3.1, Füredi's intersection semilattice lemma;
  3. Lemma 3.3 and Proposition 3.4, the rank bound ∣F∗∣≤(np)|\mathcal F^*|\le\binom np∣F∗∣≤(pn​);
  4. Lemma 3.6 and Corollary 3.7, the structure of patterns of rank kkk and k−1k-1k−1;
  5. Theorem 3.8, blowups of trees in homogeneous families of rank kkk or type 2;
  6. Theorem 3.9, the canonical partition of a path-free family;
  7. Lemmas 4.2–4.4, from kernel graphs to blowups, the edge count (k−1)∣F∣≤e(H′)(n−2k−2)(k-1)|\mathcal F|\le e(H')\binom{n-2}{k-2}(k−1)∣F∣≤e(H′)(k−2n−2​), and the Erdős–Gallai circumference bound;
  8. Theorem 4.5, exk(n,P2t+2(k))≤t(n−1k−1)+O(nk−2)\mathbf{ex}_k(n,\mathbb P_{2t+2}^{(k)})\le t\binom{n-1}{k-1}+O(n^{k-2})exk​(n,P2t+2(k)​)≤t(k−1n−1​)+O(nk−2);
  9. the bound (1), exk(n,Pℓ(k))≤(k−1)(ℓ−1)(n−1k−1)\mathbf{ex}_k(n,\mathbb P^{(k)}_\ell)\le(k-1)(\ell-1)\binom{n-1}{k-1}exk​(n,Pℓ(k)​)≤(k−1)(ℓ−1)(k−1n−1​) for every nnn;
  10. Erdős' matching theorem for large nnn (Theorem 1.1) and the rrr-intersecting Erdős–Ko–Rado theorem;
  11. Claims 2–3 of §5: a near-extremal family has a ttt-set joined in the kernel graph to all but O(n)O(\sqrt n)O(n​) vertices.

Significance

The theorem gives the exact extremal number and the unique extremal configuration for every linear path when k≥4k\ge4k≥4, and its proof yields a stability statement (Theorem 5.1 of the paper): a path-free family of nearly extremal size is close to a star family. The method is the standard template for exact results on sparse hypergraphs: delta-system decomposition, an asymptotic bound from a graph theorem applied to a kernel graph, then stability and cleaning.

The result is proved in the paper. As far as the platform's catalog shows, none of it is formalized: Mathlib has the intersecting Erdős–Ko–Rado theorem (r=1r=1r=1) but not the rrr-intersecting version with uniqueness for large nnn, not Erdős' matching theorem, not the Erdős–Gallai circumference theorem, and no delta-system machinery. A formalization would produce reusable libraries for each of these, and the first machine-checked exact hypergraph Turán number for an infinite family.

Difficulty

The obvious argument — bound the number of members through each vertex and sum — gives only O(nk−1)O(n^{k-1})O(nk−1) with a wrong leading constant, because a path-free family can have ttt vertices of almost full degree. Exactness needs three layers that do not reduce to one another. First, the intersection semilattice lemma (Lemma 3.1) is a deep structural theorem with a doubly exponential constant; it is cited, not proved, in the paper. Second, the asymptotic bound passes through the Erdős–Gallai circumference theorem, itself a nontrivial graph result. Third, the exact value requires a stability argument with several error terms (O(nk−3/2)O(n^{k-3/2})O(nk−3/2), O(n)O(\sqrt n)O(n​)) that must be balanced, followed by Erdős' matching theorem and the 2-intersecting Erdős–Ko–Rado theorem for large nnn, both with uniqueness. The structural lemmas of §§3–4 are elementary but involve greedy embeddings in which disjointness of the new vertices has to be maintained carefully.

Formalization scope

Vertices are Fin n; families are Finset (Finset (Fin n)); kkk-uniformity is 𝓕 ⊆ univ.powersetCard k. A linear path is an injective map Fin ℓ → Finset (Fin n) into the family with consecutive members meeting in exactly one vertex and non-consecutive members disjoint. The Turán number is a Finset.sup of cardinalities over all path-free kkk-uniform subfamilies, a finite attained maximum for ℓ≥1\ell\ge1ℓ≥1; at ℓ=0\ell=0ℓ=0 the feasible set is empty and Lean returns zero, which no theorem here uses. Partitions into kkk parts are part maps Fin n → Fin k; stars are finsets of distinct petals; the rank is a minimum computed with sInf, exact because intersection patterns consist of proper subsets of [k][k][k]. "Sufficiently large nnn" and every O(⋅)O(\cdot)O(⋅) are explicit existential constants chosen before nnn and the family.

Hypotheses added relative to the page: Theorem 3.8 assumes the homogeneous family is nonempty (the empty family is homogeneous for every pattern, and the proof starts from a member). Lemma 4.2 states k≥2k\ge2k≥2, implicit in the paper's definition of a blowup using k−2k-2k−2 new vertices; it excludes a false zero-threshold case at k=0k=0k=0. Lemma 4.3 is stated for an arbitrary choice of a vertex c(F)∈Fc(F)\in Fc(F)∈F in each member, a generalization of the printed lemma, whose proof uses only c(F)∈Fc(F)\in Fc(F)∈F. Bound (1) is posed for k≥2k\ge2k≥2. Claims 2–3 are stated stand-alone with the ttt-set SSS existentially quantified.

Trivializing formalizations are ruled out: the goal asserts the value and the uniqueness of the extremal family, the Turán number is a true maximum rather than a junk supremum, the linear path requires intersections of size exactly one and disjointness of non-consecutive members, and all constants are quantified before nnn.

Needed infrastructure: the delta-system method (Lemma 3.1), Erdős–Gallai for circumference, Erdős' matching theorem and rrr-intersecting Erdős–Ko–Rado for large nnn. Each is reusable well beyond this mission, and contributions of any of them are welcome.

Selected references

  • Z. Füredi, T. Jiang, R. Seiver, Exact solution of the hypergraph Turán problem for k-uniform linear paths, Combinatorica 34 (2014). arXiv:1108.1247, doi:10.1007/s00493-014-2838-4
  • Z. Füredi, On finite set-systems whose every intersection is a kernel of a star, Discrete Math. 47 (1983) 129–132. doi:10.1016/0012-365X(83)90081-X
  • P. Erdős, T. Gallai, On maximal paths and circuits of graphs, Acta Math. Acad. Sci. Hungar. 10 (1959) 337–356. doi:10.1007/BF02024498
  • P. Erdős, A problem on independent r-tuples, Ann. Univ. Sci. Budapest. Eötvös Sect. Math. 8 (1965) 93–95.
  • P. Erdős, C. Ko, R. Rado, Intersection theorems for systems of finite sets, Quart. J. Math. Oxford 12 (1961) 313–320. doi:10.1093/qmath/12.1.313
  • P. Frankl, Z. Füredi, Exact solution of some Turán-type problems, J. Combin. Theory Ser. A 45 (1987) 226–262. doi:10.1016/0097-3165(87)90016-7
  • D. Mubayi, J. Verstraëte, Minimal paths and cycles in set systems, European J. Combin. 28 (2007) 1681–1693. doi:10.1016/j.ejc.2006.07.008
19 thms1 active userReviewed
Linear OptimizationOperations ResearchOptimization·Captain: mikedeng1

Minimum Cost Flows in Graphs with Unit Capacities: When the Second Phase of a Cost Scale Starts, at Most 3λ Excess Is Left for λ = √m and 36λ for λ = n^(2/3)Research Paper

Motivation

Minimum-cost flow is a standard model for routing a quantity through a network while paying a cost on each arc. Unit capacities arise when each input arc can carry at most one unit; they include flow formulations of several matching and assignment problems. Goldberg, Kaplan, Hed and Tarjan analyze a cost-scaling algorithm for this setting. Their second-phase argument depends on a bound on the excess left in a pseudoflow when every excess vertex has undergone enough potential increases. This mission formalizes that bound, Lemma 5 of their STACS 2015 paper. The published paper gives an O(min⁡{m1/2,n2/3} mlog⁡(nC))O(\min\{m^{1/2},n^{2/3}\}\,m\log(nC))O(min{m1/2,n2/3}mlog(nC)) running-time bound for its unit-capacity algorithm; the running-time statement itself is outside this mission.

Setting

A network has a finite vertex set VVV and an arc set EEE that contains both orientations of each input arc. Write n=∣V∣n=|V|n=∣V∣. Every input arc has capacity one; its reverse has capacity zero and opposite cost. Write mmm for the number of input arcs, so ∣E∣=2m|E|=2m∣E∣=2m. The paper assumes no parallel or antiparallel input arcs, allowing an arc to be identified by its ordered endpoints. Costs are antisymmetric between orientations.

A pseudoflow fff assigns an antisymmetric quantity to each arc and respects its capacity. Its excess at vvv is the sum ef(v)e_f(v)ef​(v) of incoming flow. Positive excess means that vvv has flow to discharge; negative excess is a deficit. A circulation is a pseudoflow with zero excess at every vertex. An arc is residual for fff when its residual capacity uf(v,w)=u(v,w)−f(v,w)u_f(v,w)=u(v,w)-f(v,w)uf​(v,w)=u(v,w)−f(v,w) is positive. For vertex prices ppp, its reduced cost is cp(v,w)=c(v,w)−p(v)+p(w)c_p(v,w)=c(v,w)-p(v)+p(w)cp​(v,w)=c(v,w)−p(v)+p(w). A pseudoflow is ε\varepsilonε-optimal at ppp when every residual arc has reduced cost at least −ε-\varepsilon−ε. These are the paper's conventions on p. 408.

At the start of a cost scale, let f′f'f′ be a circulation that is 2ε2\varepsilon2ε-optimal at prices p′p'p′. During the scale, fff is a pseudoflow that is ε\varepsilonε-optimal at prices ppp. The potential increase in scale units is d(v)=(p(v)−p′(v))/εd(v)=(p(v)-p'(v))/\varepsilond(v)=(p(v)−p′(v))/ε. Put λ=min⁡{m,n2/3}\lambda=\min\{\sqrt m,n^{2/3}\}λ=min{m​,n2/3}. The second phase begins when every excess vertex has d(v)≥λd(v)\ge\lambdad(v)≥λ; the paper also states that every deficit then has d(v)=0d(v)=0d(v)=0 and that the levels d(v)d(v)d(v) are integral. The remaining excess is X(f)=∑v:ef(v)>0ef(v)X(f)=\sum_{v:e_f(v)>0}e_f(v)X(f)=∑v:ef​(v)>0​ef​(v).

The comparison graph has arcs E+={(v,w)∈E:f′(v,w)>f(v,w)}E^+=\{(v,w)\in E:f'(v,w)>f(v,w)\}E+={(v,w)∈E:f′(v,w)>f(v,w)} and vertices VVV. An excess–deficit cut Y⊆E+Y\subseteq E^+Y⊆E+ meets every path in this graph from an excess vertex to a deficit vertex. Thus a cut is measured among comparison arcs, not among arbitrary arcs of the original network. Lemmas 3 and 4 of the paper establish the level and cut bounds on which the excess estimate rests.

Formalization targets

The goal is Lemma 5 on p. 410, stated with both constants obtained in its proof. Under the cost-scale conditions above, and the divisibility conventions in footnotes 5–6, its two cases are

λ=m⟹X(f)≤3λ,\lambda=\sqrt m\quad\Longrightarrow\quad X(f)\le 3\lambda,λ=m​⟹X(f)≤3λ, λ=n2/3⟹X(f)≤36λ.\lambda=n^{2/3}\quad\Longrightarrow\quad X(f)\le36\lambda.λ=n2/3⟹X(f)≤36λ.

The second case also assumes that six divides the integer λ\lambdaλ; the first uses divisibility by three. The goal keeps the two conclusions separate so that the stronger 3λ3\lambda3λ bound is retained when m\sqrt mm​ determines the scale. Its milestone list follows the paper: Lemma 3 bounds d(v)−d(w)d(v)-d(w)d(v)−d(w) by three on an arc of E+E^+E+; Lemma 4 bounds total excess by a cut; and the proof of Lemma 5 supplies the cut bands and the two size estimates. The bands use the exact three-level and six-level ranges printed on p. 410.

Significance

The result connects a condition on price increases to a numerical bound on the amount of flow still needing discharge. Combined with the paper's time per unit of remaining excess, this is the bound needed for its second-phase running-time estimate. The two regimes show how the same cost-scale state can be controlled by either the number of input arcs or the number of vertices. Without the excess bound, the paper's argument gives no comparable limit on the remaining work at the phase transition.

The paper proves Lemma 5, but this particular unit-capacity excess argument has no matching theorem in the platform catalog checked for this mission. The network, circulation and pseudoflow interfaces are already published as reusable definitions from earlier cost-scaling work. A completed machine-checked development would add the cut argument and band counts to those interfaces. Those facts can also support other analyses that compare an entry circulation with an intermediate pseudoflow; this mission does not claim to formalize the algorithm's entire execution or its running-time model.

Difficulty

The main obstacle is linking local price inequalities to a global bound on excess. A reduced-cost inequality controls a single residual arc, while excess may move through a path and is measured over many vertices. The comparison graph G+G^+G+ is essential: on a residual arc outside E+E^+E+, the level difference used by Lemma 3 is not available. Counting all residual arcs as if they were comparison arcs would therefore not justify the cut bound. There is a separate counting issue: the represented network has both directions of each input arc, so using ∣E∣|E|∣E∣ for mmm changes the constant in the square-root case.

Formalization scope

Lean represents the network by a finite symmetric arc set with real capacities and antisymmetric real costs. A local unit-capacity predicate marks each reverse pair as capacities (1,0)(1,0)(1,0) or (0,1)(0,1)(0,1); mmm counts the capacity-one orientations. Flows are real valued as on p. 408. Integer costs on that page are generalized to real costs because Lemmas 3–5 use only antisymmetry and reduced-cost inequalities. No result here depends on cost integrality.

The formalization takes the cost-scale state as hypotheses: ε>0\varepsilon>0ε>0, entry circulation and 2ε2\varepsilon2ε-optimality, current ε\varepsilonε-optimality, integral levels, level at least λ\lambdaλ at excess vertices, and zero level at deficits. It takes λ\lambdaλ as a natural number equal to min⁡{m,n2/3}\min\{\sqrt m,n^{2/3}\}min{m​,n2/3} and records the paper's divisibility simplifications. The indexed pigeonhole milestones also require λ>0\lambda>0λ>0 so their index ranges are nonempty; the goal includes the zero-arc boundary without requiring an index. Cuts use reachability after deleting a subset of E+E^+E+, equivalent to meeting every excess-to-deficit path. The proof of Lemma 5 prints “cut in GfG_fGf​”; the formalized claim uses G+G^+G+, the graph for which Lemma 3 gives the required level control and Lemma 4 states its bound.

The definition layer, path-cut lemma and finite counting arguments are the reusable infrastructure. Contributions that establish these exact statements, their necessary elementary flow identities, or a machine-checked goal proof are in scope. Counting mmm with reverse arcs, allowing cuts outside E+E^+E+, using arbitrary real levels in place of integral ones, or replacing both cases by the weaker 36λ36\lambda36λ bound does not express this target.

Selected references

  • A. V. Goldberg, H. Kaplan, S. Hed and R. E. Tarjan, Minimum Cost Flows in Graphs with Unit Capacities, 32nd International Symposium on Theoretical Aspects of Computer Science, LIPIcs 30, pp. 406–419, 2015. doi:10.4230/LIPIcs.STACS.2015.406.
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CombinatoricsGeometry & Topology·Captain: mikedeng1

Planar Graphs Have Bounded Queue-Number 2: Every Graph of Euler Genus g Has Queue-Number at Most 4g + 49Research Paper

Motivation

A queue layout of a graph places its vertices on a line and splits its edges into classes, called queues, so that inside one queue no edge is nested inside another. The minimum number of queues is the queue-number. Queue layouts were introduced by Heath, Leighton and Rosenberg (1992) and Heath and Rosenberg (1992) as the dual of stack layouts (book embeddings): reading the vertex order from left to right, the edges of one queue are opened and closed in first-in first-out order. Queue layouts are closely tied to track layouts and, through them, to three-dimensional grid drawings of graphs (§9 of the paper).

Whether planar graphs have bounded queue-number was asked by Heath, Leighton and Rosenberg in 1992 and stayed open for 27 years. The best general bounds before 2019 grew with the number of vertices: O(n)O(\sqrt n)O(n​) by Heath, Leighton and Rosenberg (1992), O(log⁡2n)O(\log^2 n)O(log2n) by Di Battista, Frati and Pach (2013), and O(log⁡n)O(\log n)O(logn) by Dujmović (2015); for Euler genus ggg, Dujmović, Morin and Wood (2017) had O(g+log⁡n)O(g + \log n)O(g+logn). Dujmović, Joret, Micek, Morin, Ueckerdt and Wood (arXiv:1904.04791, J. ACM 2020) proved that every planar graph has queue-number at most 494949 (Theorem 1), and extended the result to every graph embeddable in a fixed surface (Theorem 2) and to every proper minor-closed class (Theorem 3).

This mission is Theorem 2: graphs of Euler genus ggg have queue-number at most 4g+494g + 494g+49. It is the second mission of a two-mission series; the first poses Theorem 1.

Setting

All graphs are finite and simple. A linear ordering ⪯\preceq⪯ of the vertex set V(G)V(G)V(G) is fixed. Two edges vwvwvw and xyxyxy with four distinct ends nest if v≺x≺y≺wv \prec x \prec y \prec wv≺x≺y≺w (after renaming the ends). A queue is a set of edges no two of which nest. A kkk-queue layout of GGG is a linear ordering together with a partition of E(G)E(G)E(G) into kkk queues, and qn⁡(G)≤k\operatorname{qn}(G) \le kqn(G)≤k means that a kkk-queue layout exists.

A layering of GGG is an ordered partition (V0,V1,… )(V_0, V_1, \dots)(V0​,V1​,…) of V(G)V(G)V(G) such that every edge joins two vertices of the same or of consecutive layers. A BFS layering takes one root rrr in each connected component and puts vvv in layer dist⁡(r,v)\operatorname{dist}(r, v)dist(r,v). A BFS spanning tree of a connected graph GGG rooted at rrr is a spanning tree TTT with dist⁡T(r,v)=dist⁡G(r,v)\operatorname{dist}_T(r, v) = \operatorname{dist}_G(r, v)distT​(r,v)=distG​(r,v) for every vvv. A path in TTT is vertical if the distance to rrr grows by exactly one at every step.

The Euler genus of the orientable surface with hhh handles is 2h2h2h, and that of the non-orientable surface with ccc cross-caps is ccc; the Euler genus of a graph is the least Euler genus of a surface it embeds in (footnote 2 of the paper). Planar graphs are exactly the graphs of Euler genus 000. Combinatorially, an embedding of a graph is described by an embedding scheme: a cyclic order of the edges at each vertex (a rotation system) together with a sign on every edge (twisted or not). Tracing faces through the scheme gives a face count fff, and with nnn vertices, mmm edges and ccc components the scheme has Euler genus 2c−n+m−f2c - n + m - f2c−n+m−f.

Formalization targets

Goal: Theorem 2 with the paper's bound

Euler genus of G≤g ⟹ qn⁡(G)≤4g+49.\text{Euler genus of } G \le g \ \Longrightarrow\ \operatorname{qn}(G) \le 4g + 49 .Euler genus of G≤g ⟹ qn(G)≤4g+49.

The paper states Theorem 2 as qn⁡(G)=O(g)\operatorname{qn}(G) = O(g)qn(G)=O(g) and proves the explicit bound 4g+494g + 494g+49 in §5. The goal poses that explicit bound, for every finite graph, connected or not.

Milestones

  1. Lemma 18 (p. 18). Every BFS layering (V0,V1,… )(V_0, V_1, \dots)(V0​,V1​,…) of a planar graph GGG admits a 494949-queue layout whose vertex ordering lists V0V_0V0​, then V1V_1V1​, then V2V_2V2​, and so on.
  2. Lemma 21 (p. 19). If GGG is connected with Euler genus at most ggg and TTT is a BFS spanning tree with layering (Vi)(V_i)(Vi​), there is a connected (or empty) subgraph ZZZ with at most 2g2g2g vertices in each layer such that G−V(Z)G - V(Z)G−V(Z) is planar and sits inside a connected planar graph G+G^+G+ whose own BFS layering (Wi)(W_i)(Wi​) satisfies Wi∩(V(G)∖V(Z))=Vi∖V(Z)W_i \cap (V(G)\setminus V(Z)) = V_i \setminus V(Z)Wi​∩(V(G)∖V(Z))=Vi​∖V(Z); vertical paths of the BFS tree of G+G^+G+ restrict to vertical paths of TTT.
  3. The 4g4g4g-queue step (p. 23). If GGG has a layering with at most 2g2g2g vertices of ZZZ per layer and G−ZG - ZG−Z has a 494949-queue layout ordered layer by layer, then qn⁡(G)≤4g+49\operatorname{qn}(G) \le 4g + 49qn(G)≤4g+49.

Significance

The result. Theorem 2 settles boundedness of the queue-number for every class of graphs embeddable in a fixed surface, with a bound linear in the Euler genus. Through the connection between queue-number and track-number, the paper derives from it (§9, Theorems 54 and 58) that a graph of Euler genus ggg with nnn vertices has track-number gO(g4/7)g^{O(g^{4/7})}gO(g4/7) and a three-dimensional grid drawing of volume gO(g4/7)ng^{O(g^{4/7})} ngO(g4/7)n, linear in nnn for every fixed surface. It is also a step on the paper's route to Theorem 3 (proper minor-closed classes), since graphs on surfaces are the basic pieces of the Robertson–Seymour graph minor structure theorem. Lemma 21 is of independent use: it reduces a graph on a surface to a planar graph while controlling BFS layers.

Formalizing it. The result is proved; as far as is known it has no machine-checked proof. A formalization needs queue layouts, layerings, BFS layerings and vertical paths, and, independently of queues, a usable combinatorial theory of graphs on surfaces, which Lean's Mathlib does not have. The Euler genus definition of this mission (embedding schemes and face tracing) and Lemma 21 are reusable well beyond queue layouts.

Difficulty

The obvious reduction, deleting few vertices to make the graph planar, is not enough: a planar subgraph alone does not control how the deleted vertices interact with a queue layout. The deleted set must be thin in every BFS layer, and the planar remainder must carry a BFS layering compatible with the original one, which is what Lemma 21 provides. Its proof cuts the surface along a subgraph made of tree paths and must show that the cut graph is planar by an Euler-characteristic count over face-tracing data, and that distances from a new root agree with the old BFS distances. In Lean, the main obstacle is the surface side: Euler's formula for embedding schemes, the cutting operation on rotation systems, and the passage between the combinatorial notion of Euler genus and the drawing-based notion of planarity. The planar input, Lemma 18, is itself the whole of mission 1's argument (Theorem 15 with Lemmas 5 and 8).

Formalization scope

  • Graphs are SimpleGraph V on a finite type V : Type with decidable equality and adjacency.
  • "qn⁡(G)≤k\operatorname{qn}(G) \le kqn(G)≤k" is HasQueueLayout G k: an injective map ord : V → ℕ (the linear ordering) and an assignment of the edges of GGG (G.edgeSet, not all unordered pairs) to Fin k such that no two edges in one queue nest; nesting uses strict inequalities, so edges sharing an end never nest. Queues may be empty.
  • A layering is a layer function L : V → ℕ; a BFS layering takes one root per component. Vertical paths are nonempty lists indexed from 000 with distance d+id + id+i, d≥0d \ge 0d≥0.
  • Euler genus is combinatorial: EulerGenusLE G g asks for an embedding scheme (rotation permutation of the darts, cyclic at every vertex, and a symmetric edge signature) whose Euler genus 2c−n+m−f2c - n + m - f2c−n+m−f, computed in Z\mathbb ZZ, is at most ggg. The face count is half the number of face-tracing orbits plus the number of isolated vertices. Equivalence with footnote 2's topological definition is the Heffter–Edmonds–Ringel rotation principle and its non-orientable extension, with additivity over components; it is not part of the mission.
  • Planarity is the published RobertsonSeymour1986.GM5.IsPlanar: a crossing-free drawing in R2\mathbb R^2R2 by simple arcs. "Euler genus 000 iff planar" is a theorem connecting the two definitions, not built into either.
  • "Euler genus ggg" is read as "at most ggg" throughout; every bound is monotone in ggg. In Lemma 21, "ZZZ is connected" reads "empty or connected" (the paper takes Z=∅Z = \emptysetZ=∅ when g=0g = 0g=0), and the restriction of a vertical path may be empty.
  • Trivializing formalizations are ruled out: queues indexed on all pairs Sym2 V would deny edgeless graphs a 000-queue layout; non-strict nesting would forbid adjacent edges in one queue; a drawing that tolerates crossings would make every graph planar; a face count without halving, or without isolated vertices, would make the Euler genus too small or too large (the sanity checks confirm K3K_3K3​ has a scheme of Euler genus 000, a twisted K3K_3K3​ one of genus 111, and two isolated vertices genus 000).
  • Welcome contributions: Euler's formula for embedding schemes, face-tracing lemmas, the planar case (Lemma 18, via mission 1's results), the cutting construction of Lemma 21, and the purely combinatorial 4g4g4g-queue step.

Selected references

  • V. Dujmović, G. Joret, P. Micek, P. Morin, T. Ueckerdt, D. R. Wood, Planar graphs have bounded queue-number, J. ACM 67(4), 2020; arXiv:1904.04791v5. https://arxiv.org/abs/1904.04791 , https://doi.org/10.1145/3385731
  • L. S. Heath, F. T. Leighton, A. L. Rosenberg, Comparing queues and stacks as mechanisms for laying out graphs, SIAM J. Discrete Math. 5(3), 1992. https://doi.org/10.1137/0405031
  • L. S. Heath, A. L. Rosenberg, Laying out graphs using queues, SIAM J. Comput. 21(5), 1992. https://doi.org/10.1137/0221055
  • V. Dujmović, Graph layouts via layered separators, J. Combin. Theory Ser. B 110, 2015. https://doi.org/10.1016/j.jctb.2014.07.005
  • V. Dujmović, P. Morin, D. R. Wood, Layered separators in minor-closed graph classes with applications, J. Combin. Theory Ser. B 127, 2017. https://doi.org/10.1016/j.jctb.2017.05.006
  • B. Mohar, C. Thomassen, Graphs on Surfaces, Johns Hopkins University Press, 2001.
9 thms1 active userReviewed
ProbabilityRandom Matrix Theory·Captain: mikedeng1

Spectral Statistics of Erdős–Rényi Graphs I: Local Semicircle Law: Local Semicircle Law for the Sparse Noncentered Matrix A = H + f|e⟩⟨e| Down to Spectral Scale η ≈ 1/NResearch Paper

Motivation

The adjacency matrix of an Erdős–Rényi random graph contains a large deterministic mean as well as random fluctuations. The mean affects the largest eigenvalue, while the centered fluctuations determine much of the remaining spectrum. A local spectral law must describe both pieces at a resolution fine enough to distinguish eigenvalues within a narrow energy window. Erdős, Knowles, Yau and Yin established such estimates for a broader class of sparse symmetric matrices and their noncentered rank-one deformations in Spectral statistics of Erdős–Rényi graphs I. This mission formalizes the paper's Theorem 2.9, with the centered-matrix law and eleven intermediate results of the paper as milestones.

The paper treats the graph matrix through a model that also allows other sparse distributions. Its parameters qNq_NqN​ and fNf_NfN​ separate sparsity from the size of the deterministic mean. That separation matters: the normalized resolvent trace can obey a local semicircle law even when the deformation produces a distinguished top eigenvalue. The formal target therefore concerns the deformed matrix itself, rather than replacing it with the centered matrix.

Setting

For each size NNN, let HN=(hij)H_N=(h_{ij})HN​=(hij​) be a real symmetric random matrix. Entries indexed by the upper triangle are independent, centered, and have variance 1/N1/N1/N. Their higher moments obey

E∣hij∣p≤CpNqNp−2,3≤p≤(log⁡N)A0log⁡log⁡N.\mathbf E|h_{ij}|^p\le\frac{C^p}{Nq_N^{p-2}},\qquad 3\le p\le(\log N)^{A_0\log\log N}.E∣hij​∣p≤NqNp−2​Cp​,3≤p≤(logN)A0​loglogN.

The sparsity parameter qNq_NqN​ lies between (log⁡N)3ξN(\log N)^{3\xi_N}(logN)3ξN​ and CNC\sqrt NCN​, and the logarithmic parameter satisfies 1+a0≤ξN≤A0log⁡log⁡N1+a_0\le\xi_N\le A_0\log\log N1+a0​≤ξN​≤A0​loglogN, with a0>0a_0>0a0​>0 and A0≥10A_0\ge10A0​≥10. Define eN=N−1/2(1,…,1)e_N=N^{-1/2}(1,\ldots,1)eN​=N−1/2(1,…,1) and the noncentered matrix AN=HN+fN∣eN⟩⟨eN∣A_N=H_N+f_N|e_N\rangle\langle e_N|AN​=HN​+fN​∣eN​⟩⟨eN​∣, where 0≤fN≤NC0\le f_N\le N^C0≤fN​≤NC. The projection ∣eN⟩⟨eN∣|e_N\rangle\langle e_N|∣eN​⟩⟨eN​∣ has every entry equal to 1/N1/N1/N. These are Definitions 2.1 and 2.2 of the source paper, pp. 5–6.

For a complex number z=E+iηz=E+i\etaz=E+iη with η>0\eta>0η>0, the resolvents are G(z)=(HN−zI)−1G(z)=(H_N-zI)^{-1}G(z)=(HN​−zI)−1 and G~(z)=(AN−zI)−1\widetilde G(z)=(A_N-zI)^{-1}G(z)=(AN​−zI)−1. Their normalized traces are m(z)=N−1Tr⁡G(z)m(z)=N^{-1}\operatorname{Tr}G(z)m(z)=N−1TrG(z) and m~(z)=N−1Tr⁡G~(z)\widetilde m(z)=N^{-1}\operatorname{Tr}\widetilde G(z)m(z)=N−1TrG(z). The comparison object is the semicircle density ρsc(x)=(2π)−1[4−x2]+\rho_{\mathrm{sc}}(x)=(2\pi)^{-1}\sqrt{[4-x^2]_+}ρsc​(x)=(2π)−1[4−x2]+​​ and its Stieltjes transform msc(z)=∫Rρsc(x)/(x−z) dxm_{\mathrm{sc}}(z)=\int_{\mathbb R}\rho_{\mathrm{sc}}(x)/(x-z)\,dxmsc​(z)=∫R​ρsc​(x)/(x−z)dx. Put κE=∣∣E∣−2∣\kappa_E=\bigl||E|-2\bigr|κE​=​∣E∣−2​ and D={E+iη:∣E∣≤Σ, 0<η≤3}D=\{E+i\eta:|E|\le\Sigma,\ 0<\eta\le3\}D={E+iη:∣E∣≤Σ, 0<η≤3} for a fixed Σ≥3\Sigma\ge3Σ≥3. These definitions appear in Section 2, pp. 7–9.

An NNN-dependent event has (ξ,ν)(\xi,\nu)(ξ,ν)-high probability when its failure probability is at most exp⁡[−ν(log⁡N)ξN]\exp[-\nu(\log N)^{\xi_N}]exp[−ν(logN)ξN​] for all sufficiently large NNN. The spectral estimates below refer to single events on which the claimed bound holds at every z∈Dz\in Dz∈D simultaneously.

Formalization targets

Main goal: the local law for A

Theorem 2.9 assumes ξN∼(A0/2)log⁡log⁡N\xi_N\sim(A_0/2)\log\log NξN​∼(A0​/2)loglogN and qN≥(log⁡N)C1ξNq_N\ge(\log N)^{C_1\xi_N}qN​≥(logN)C1​ξN​. Its first conclusion is that, with (ξ,ν)(\xi,\nu)(ξ,ν)-high probability, every z=E+iη∈Dz=E+i\eta\in Dz=E+iη∈D satisfies

∣m~(z)−msc(z)∣≤(log⁡N)C2ξN(min⁡{1qN2κE+η,1qN}+1Nη).|\widetilde m(z)-m_{\mathrm{sc}}(z)|\le(\log N)^{C_2\xi_N}\left(\min\left\{\frac1{q_N^2\sqrt{\kappa_E+\eta}},\frac1{q_N}\right\}+\frac1{N\eta}\right).∣m(z)−msc​(z)∣≤(logN)C2​ξN​(min{qN2​κE​+η​1​,qN​1​}+Nη1​).

Under the additional bound fN≤C0Nf_N\le C_0\sqrt NfN​≤C0​N​, its second conclusion controls every entry at every z∈Dz\in Dz∈D:

∣G~ij(z)−δijmsc(z)∣≤(log⁡N)C2ξN(1qN+Im⁡msc(z)Nη+1Nη).|\widetilde G_{ij}(z)-\delta_{ij}m_{\mathrm{sc}}(z)|\le(\log N)^{C_2\xi_N}\left(\frac1{q_N}+\sqrt{\frac{\operatorname{Im}m_{\mathrm{sc}}(z)}{N\eta}}+\frac1{N\eta}\right).∣Gij​(z)−δij​msc​(z)∣≤(logN)C2​ξN​(qN​1​+NηImmsc​(z)​​+Nη1​).

The constants C1,C2C_1,C_2C1​,C2​ are universal and positive. The probability exponent ν>0\nu>0ν>0 may depend on the fixed parameters named in the theorem. Both conclusions, including the extra condition on fNf_NfN​ in the second, are retained from Theorem 2.9, pp. 9–10.

Milestones

The milestones follow the paper's own sequence of numbered results.

  1. Properties of mscm_{\mathrm{sc}}msc​ (Lemma 3.2): ∣msc∣∼1|m_{\mathrm{sc}}|\sim1∣msc​∣∼1, ∣1−msc2∣∼κE+η|1-m_{\mathrm{sc}}^2|\sim\sqrt{\kappa_E+\eta}∣1−msc2​∣∼κE​+η​, and the size of Im⁡msc\operatorname{Im}m_{\mathrm{sc}}Immsc​ inside and outside the spectrum.
  2. Resolvent identities for minors (Lemma 3.4) and the self-consistent equation Gii=1/(−z−msc−([v]−Υi))G_{ii}=1/(-z-m_{\mathrm{sc}}-([v]-\Upsilon_i))Gii​=1/(−z−msc​−([v]−Υi​)) (Lemma 3.10); both are deterministic.
  3. Large deviation bounds for linear and quadratic forms in independent variables with the sparse moment decay (Lemma 3.8 (ii)).
  4. The weak local semicircle law for HHH (Theorem 3.1) on DL={∣E∣≤Σ, (log⁡N)LN−1≤η≤3}D_L=\{|E|\le\Sigma,\ (\log N)^LN^{-1}\le\eta\le3\}DL​={∣E∣≤Σ, (logN)LN−1≤η≤3}, L≥8ξL\ge8\xiL≥8ξ.
  5. Fluctuation averaging (Lemma 4.1 (4.2)): the average [Z]=N−1∑iZi[Z]=N^{-1}\sum_iZ_i[Z]=N−1∑i​Zi​ of the fluctuation terms is much smaller than each ZiZ_iZi​.
  6. The a priori norm bound ∥H∥≤2+(log⁡N)ξq−1/2\|H\|\le2+(\log N)^\xi q^{-1/2}∥H∥≤2+(logN)ξq−1/2 (Lemma 4.3) and the strong local law for HHH (Theorem 2.8, the analogue of the goal for f=0f=0f=0).
  7. Interlacing of the eigenvalues of HHH and AAA (Lemma 6.1) and the near-orthogonality ∑α≠N∣⟨vα,e⟩∣2=O(f−2)\sum_{\alpha\ne N}|\langle v_\alpha,e\rangle|^2=O(f^{-2})∑α=N​∣⟨vα​,e⟩∣2=O(f−2) of eee to the non-top eigenvectors of AAA (Corollary 6.7).
  8. The trace comparison ∣Λ~−Λ∣≤π/(Nη)|\widetilde\Lambda-\Lambda|\le\pi/(N\eta)∣Λ−Λ∣≤π/(Nη) (Lemma 7.1), which yields the first conclusion of the goal from Theorem 2.8.
  9. Entrywise control of G~\widetilde GG on the event Ω~(η)\widetilde\Omega(\eta)Ω(η) that Λ~d+Λ~o≤(log⁡N)−ξ\widetilde\Lambda_d+\widetilde\Lambda_o\le(\log N)^{-\xi}Λd​+Λo​≤(logN)−ξ on the line Im⁡z=η\operatorname{Im}z=\etaImz=η: the off-diagonal bound Λ~o≤CΦ\widetilde\Lambda_o\le C\PhiΛo​≤CΦ (Proposition 7.6 (7.14)) and the diagonal bounds of Lemma 7.7, with Φ=(log⁡N)ξ/q+(log⁡N)2ξ(Im⁡m~/(Nη)+1/(Nη))\Phi=(\log N)^\xi/q+(\log N)^{2\xi}(\sqrt{\operatorname{Im}\widetilde m/(N\eta)}+1/(N\eta))Φ=(logN)ξ/q+(logN)2ξ(Imm/(Nη)​+1/(Nη)).

Significance

The trace estimate gives quantitative control over spectral mass at local scales, including near the edges E=±2E=\pm2E=±2. The entrywise estimate says more: each resolvent entry is close to the diagonal semicircle approximation with an error that records both sparsity and finite-size effects. The paper's later results derive density-of-states and eigenvector statements from these local controls. Those consequences are outside this mission's goal.

The intended formal development supplies reusable definitions of sparse symmetric ensembles, high-probability events, semicircle transforms, and complex resolvents, followed by exact statements of the paper's estimates. Mathlib contains no local semicircle law, for Wigner or for sparse matrices, and no Stieltjes transform of the semicircle distribution; the local law, the large deviation bounds and the interlacing lemma are each reusable beyond this paper.

Difficulty

The deterministic rank-one comparison controls the normalized trace but does not by itself control each entry of G~\widetilde GG. The entrywise estimate must account for the deformation and for fluctuations caused by sparse entries. Near the spectral edges, the stability scale of the semicircle transform changes with κE+η\kappa_E+\etaκE​+η; a bound that is uniform in the bulk does not automatically retain the paper's edge behavior. The theorem also asks for one event covering a continuum of zzz values, so a collection of separate fixed-zzz probability bounds has the wrong quantifier order. These are substantive parts of Sections 3–7, beyond algebraic manipulation of resolvents.

Formalization scope

Lean represents all HNH_NHN​ on one probability space and takes their entries to be real, symmetric, measurable random variables, independent in the upper triangle. Conditions involving log⁡log⁡N\log\log NloglogN and the moments hold for all sufficiently large NNN; this avoids impossible small-NNN parameter bounds and matches the asymptotic theorem. The moment order ppp is real, as in the paper. Each finite expectation has an explicit integrability condition. A single positive constant CCC is used for the moment, sparsity, and deformation bounds: replacing several fixed constants by their maximum preserves all three inequalities.

Matrix indices use Fin N rather than the paper's 1,…,N1,\ldots,N1,…,N. The resolvent is a complex matrix formed by coercing the real matrix before subtracting zIzIzI. Every result that uses it restricts η>0\eta>0η>0 eventually or explicitly; its inverse at a singular input is never used to make a claim. The semicircle transform is defined by the paper's integral, and 4−x2\sqrt{4-x^2}4−x2​ uses Lean's nonnegative square root, implementing the positive part. The high-probability predicate evaluates the measure of arbitrary sets, so it can state the intersection over all zzz without a separate measurability assumption on that intersection. Positive qNq_NqN​ and positive square-root arguments follow eventually from the model and z∈Dz\in Dz∈D.

The deterministic Lemmas 3.2, 3.4, 3.10, 6.1 and 7.1 are stated for every real symmetric matrix and every zzz with η>0\eta>0η>0 (Lemma 3.2 on DDD); the paper's DLD_LDL​ is contained there. Minors keep the original index names, and Gkl(T)G^{(\mathbb T)}_{kl}Gkl(T)​ is only used for k,l∉Tk,l\notin\mathbb Tk,l∈/T. ZiZ_iZi​ is the explicit expression of (3.15). Ordered eigenvalues are the sorted roots of the characteristic polynomial. Statements the paper makes "for fixed zzz" (Lemmas 4.1, 7.7, Proposition 7.6) are uniform in zzz: one threshold N0N_0N0​ serves every zzz. The unnamed constants CCC, ν\nuν, KKK are chosen after the fixed parameters and before the random matrices; the universal C1,C2C_1,C_2C1​,C2​ come first.

Trivializing encodings are ruled out: every resolvent is taken at η>0\eta>0η>0, so the junk inverse of a singular matrix never enters; the model is satisfiable (independent Rademacher signs divided by N\sqrt NN​ with qN=Nq_N=\sqrt NqN​=N​ and ξN=(A0/2)log⁡log⁡N\xi_N=(A_0/2)\log\log NξN​=(A0​/2)loglogN satisfy it for large NNN); each event of the goal is one intersection over all z∈Dz\in Dz∈D, not a family of pointwise bounds; the constants appear in the paper's order; and the goal is about A=H+f∣e⟩⟨e∣A=H+f|e\rangle\langle e|A=H+f∣e⟩⟨e∣ with fff allowed to be large, so it does not reduce to Theorem 2.8.

Part (iii) of Lemma 3.8 is not a milestone: the printed (3.22) omits the diagonal variance term (N−2∑i∣Bii∣2)1/2(N^{-2}\sum_i|B_{ii}|^2)^{1/2}(N−2∑i​∣Bii​∣2)1/2, without which it fails for Rademacher variables with q≍Nq\asymp\sqrt Nq≍N​. Contributions of proofs of any milestone, and of the missing groundwork (Stieltjes transforms of measures, Schur complements of resolvents, moment bounds for sums of independent variables), are welcome.

Selected references

  • L. Erdős, A. Knowles, H.-T. Yau and J. Yin, Spectral statistics of Erdős–Rényi graphs I: Local semicircle law, Annals of Probability 41(3B), 2013, pp. 2279–2375. arXiv:1103.1919v5; DOI:10.1214/11-AOP734.
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Combinatorics·Captain: mikedeng1

Planar Graphs Have Bounded Queue-Number 1: Every Planar Graph Has a 49-Queue LayoutResearch Paper

Motivation

A queue layout represents a graph by placing its vertices in a line and assigning edges to queues. The order and assignment must prevent nested edges from sharing a queue. This gives a graph parameter that measures how much queue capacity is needed for a particular graph. Heath, Leighton, and Rosenberg asked whether one fixed number of queues suffices for every planar graph. The question remained open while bounds depending on the graph's number of vertices, degree, or treewidth were known. Dujmović, Joret, Micek, Morin, Ueckerdt, and Wood resolved it by proving a uniform bound of 49. Their paper also develops layered partitions, a structural tool used beyond queue layouts. These claims and the historical account appear in the source paper, pp. 3–5 and 16.

Setting

A finite simple graph GGG has vertices V(G)V(G)V(G) and unordered edges E(G)E(G)E(G). Choose a linear order of V(G)V(G)V(G). Two disjoint edges nest when their endpoints appear as a<b<c<da<b<c<da<b<c<d, with one edge joining aaa to ddd and the other joining bbb to ccc. A queue is a set of edges with no nested pair. A kkk-queue layout is a vertex order together with an assignment of every edge to one of kkk queues; qn⁡(G)≤k\operatorname{qn}(G)\le kqn(G)≤k means such a layout exists. Edges sharing an endpoint cannot nest. Empty queues are allowed, so an edgeless graph can use zero queues. These are the paper's definitions in §1, pp. 3–4.

A layering assigns vertices to layers V0,V1,…V_0,V_1,\ldotsV0​,V1​,… such that endpoints of an edge lie in the same or adjacent layers. A BFS layering starts from one selected root in each connected component and places each vertex in the layer equal to its graph distance from that root. A vertex partition divides V(G)V(G)V(G) into disjoint nonempty parts. Its quotient graph G/PG/\mathcal PG/P has those parts as vertices; distinct parts are adjacent if an edge of GGG joins them. An HHH-partition may index the parts by vertices of another graph HHH: the quotient's edges must be represented by edges of HHH, while an indexed part may be empty. A partition has layered width at most ℓ\ellℓ with respect to a fixed layering if every part contains at most ℓ\ellℓ vertices of every layer. The exact definitions are in §2, pp. 7–9.

A graph is planar when it can be drawn in the plane with vertices at distinct points and edges as simple arcs that meet only at shared endpoints. A tree decomposition covers every vertex and edge of a graph by bags indexed by a tree, with the bags containing each vertex forming a connected subtree. Treewidth at most three means that a tree decomposition exists with every bag of size at most four. The paper uses these standard notions in §2 and §4, pp. 7, 12, and 16.

Formalization targets

Queue bound for planar graphs

The goal is the explicit upper bound the paper derives for its Theorem 1:

∀G finite and planar,qn⁡(G)≤49.\forall G\text{ finite and planar},\qquad \operatorname{qn}(G)\le49.∀G finite and planar,qn(G)≤49.

The printed theorem says that planar queue-number is bounded, and the authors state 49 immediately after it. A bound that depends on ∣V(G)∣|V(G)|∣V(G)∣ would not express the result. The goal uses the paper's own constant, as given in Theorem 1 and the following sentence, p. 5.

Layered partition and transfer bounds

The structural target is Theorem 15, p. 16: for every BFS layering of a planar graph, there is a partition of layered width at most three whose quotient remains planar and has treewidth at most three. The queue transfer target is Lemma 8, p. 10:

qn⁡(G)≤3ℓqn⁡(H)+⌊3ℓ2⌋\operatorname{qn}(G)\le 3\ell\operatorname{qn}(H)+ \left\lfloor\frac{3\ell}{2}\right\rfloorqn(G)≤3ℓqn(H)+⌊23ℓ​⌋

when GGG has an HHH-partition of layered width ℓ\ellℓ. Its layout orders vertices layer by layer. The milestone list also includes the rainbow characterization, the blowup lemma, the complete-graph queue number, and the five-queue bound for planar graphs of treewidth at most three. These are the stated supporting results along the route to 49 in §2–§4.1, pp. 7 and 10–16.

Significance

The goal establishes a constant queue bound for all planar graphs, independent of their size, degree, and treewidth. Theorem 15 supplies a reusable structural statement: even if the original graph has large treewidth, its quotient by small layer intersections has treewidth at most three while remaining planar. The paper goes on to treat graphs of bounded Euler genus and proper minor-closed classes, and describes connections with graph products and other graph parameters in its abstract and introduction, pp. 1 and 5.

The result is proved in the cited paper. This mission records Lean statements of the goal and six supporting milestones; their proofs remain to be formalized. A completed development would connect finite graph orders, nested-edge combinatorics, layered partitions, planar drawings, and tree decompositions in one machine-checked chain. The queue and partition definitions can also support later work on graph classes beyond the planar case.

Difficulty

A vertex order chosen without regard to a graph's structure can contain large rainbows, forcing many queues in that order. Planarity alone does not specify an order or an edge assignment, and a direct bound from ordinary treewidth does not cover planar graphs with arbitrarily large treewidth. The central structural requirement is to find a partition whose parts remain small in each BFS layer while its quotient has low treewidth. The queue transfer lemma must retain a layer-by-layer vertex order and account for edges within parts and between parts. The paper, pp. 10–11 and 16, identifies these as distinct constraints in its stated results.

Formalization scope

Vertices form a finite type and edges are those of a Mathlib simple graph. A vertex order is an injective map into N\mathbb NN; strict inequalities on four endpoints encode nesting. Queue assignments are functions on the graph's edge set, so the empty graph's zero-queue case is present. The statements use an existence predicate for qn⁡(G)≤k\operatorname{qn}(G)\le kqn(G)≤k, without taking a numerical infimum. Layerings are functions to N\mathbb NN, with BFS roots chosen separately for disconnected components. Theorem 15 uses a partition into nonempty parts, and the quotient has exactly those parts as vertices. Treewidth at most three uses a published tree-decomposition definition with bags of size at most four.

The locally checked drawing definition has the same fields as the published plane-drawing definition; the published module was unavailable from the local index in this run. Planarity means existence of a crossing-free drawing, not a drawing that permits crossings. The formulation does not use a universal edge carrier that would rule out zero queues, a non-strict nesting test, or a default numerical treewidth. The near-triangulation, Sperner, and tripod statements from the paper's deeper proof are outside this proposal's milestone scope; the partition theorem remains a full milestone. Solvers may contribute the omitted geometric and tree-decomposition infrastructure when proving it.

Selected references

  • V. Dujmović, G. Joret, P. Micek, P. Morin, T. Ueckerdt, and D. R. Wood, Planar graphs have bounded queue-number, Journal of the ACM 67(4), 2020. arXiv:1904.04791v5; DOI:10.1145/3385731.
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CombinatoricsLinear algebra·Captain: mikedeng1

A Harary-Sachs Theorem for Hypergraphs: The Codegree-d Coefficient of the Characteristic Polynomial of a k-Graph as a Weighted Sum over Its Veblen InfragraphsResearch Paper

Motivation

The characteristic polynomial of an ordinary graph records spectral information in coefficients that can also be described by finite graph configurations. The classical Harary–Sachs theorem is an instance of this connection. For a uniform hypergraph, the adjacency object is a tensor rather than a matrix, and its characteristic polynomial is defined through a multivariate resultant. Clark and Cooper's hypergraph theorem gives a corresponding coefficient formula. It identifies the configurations that replace the elementary subgraphs of the graph case and assigns them weights determined by directed Eulerian structures. The result makes a tensor spectral invariant accessible through finite combinatorial objects.

The article appeared as Gregory J. Clark and Joshua N. Cooper, A Harary-Sachs theorem for hypergraphs, Journal of Combinatorial Theory, Series B (2021), DOI 10.1016/j.jctb.2021.01.002. This mission follows the pinned arXiv version 2, whose page and theorem numbers are used below.

Setting

A simple kkk-graph H=([n],E)\mathcal H=([n],E)H=([n],E) has a fixed vertex set of nnn labeled vertices and a set EEE of distinct edges, each containing exactly kkk vertices. Isolated vertices remain part of [n][n][n]. The normalized adjacency hypermatrix AH\mathcal A_{\mathcal H}AH​ gives an ordered kkk-tuple the value 1/(k−1)!1/(k-1)!1/(k−1)! when its underlying set is an edge, and zero otherwise. This normalization makes every ordering of the other k−1k-1k−1 vertices contribute the intended total weight.

The characteristic polynomial ϕ(H)\phi(\mathcal H)ϕ(H) is the multivariate resultant of the coordinates of (λI−AH)xk−1(\lambda\mathcal I-\mathcal A_{\mathcal H})x^{k-1}(λI−AH​)xk−1. The resultant is characterized by three conditions: it vanishes exactly when homogeneous coordinate polynomials have a nonzero common complex root, it evaluates to one on the coordinate monomials, and it is irreducible over the complex coefficient field. Its degree in λ\lambdaλ is t=n(k−1)n−1t=n(k-1)^{n-1}t=n(k−1)n−1. The codegree-ddd coefficient ϕd(H)\phi_d(\mathcal H)ϕd​(H) is the coefficient of λt−d\lambda^{t-d}λt−d; this degree is fixed from the source theorem and is not read from an implementation of ϕ\phiϕ.

A multi-hypergraph SSS is a multiset of kkk-element edges. It is Veblen when the degree of every vertex is divisible by kkk. Its flattening discards repeated edges, so SSS is an infragraph of H\mathcal HH when every edge of its flattening belongs to EEE. Components use only vertices covered by edges; a labeled object may still sit in a larger ambient vertex set with unused vertices.

A rooting of SSS is a sequence of its edges, each assigned a root vertex in that edge, with roots in nondecreasing order. An edge rooted at uuu contributes a directed arc u→vu\to vu→v to every other vertex vvv of the edge. Summing these arcs with multiplicity produces a directed multigraph DRD_RDR​. An Euler rooting has balanced in- and out-degrees and connected positive-degree vertices. Its arborescence number τ(DR)\tau(D_R)τ(DR​) counts spanning trees of these active vertices directed toward a specified root, with parallel arcs distinguished. For connected SSS, its associated coefficient is

CS=∑R: DR Eulerianτ(DR)∏v∈V(DR)deg⁡−(v).C_S=\sum_{R:\,D_R\text{ Eulerian}}\frac{\tau(D_R)}{\prod_{v\in V(D_R)}\deg^-(v)}.CS​=R:DR​ Eulerian∑​∏v∈V(DR​)​deg−(v)τ(DR​)​.

For disconnected SSS, CSC_SCS​ is the product of the coefficients of its connected components. These conventions follow Definitions 6–10 in the source.

Formalization targets

Differential trace and Euler rootings

The supporting targets identify when a monomial differential operator contributes to a matrix trace, and show that an Euler rooting determines a connected Veblen multi-hypergraph. The operator identity is counted by Euler tours with distinguishable parallel arcs. The resultant-to-trace identity quoted in the paper is also a milestone:

ϕd(H)=Pd(−Tr⁡1(H)/1,…,−Tr⁡d(H)/d).\phi_d(\mathcal H)=P_d\left(-\operatorname{Tr}_1(\mathcal H)/1,\ldots,-\operatorname{Tr}_d(\mathcal H)/d\right).ϕd​(H)=Pd​(−Tr1​(H)/1,…,−Trd​(H)/d).

Here PdP_dPd​ is the paper's sum over positive compositions of ddd, and Tr⁡j\operatorname{Tr}_jTrj​ is its differential trace from Equation (1).

Harary–Sachs coefficient formula

The goal uses the labeled form of Theorem 14. Let Tm(d,H)\mathscr T_m(d,\mathcal H)Tm​(d,H) be the set of ordered mmm-tuples (S1,…,Sm)(S_1,\ldots,S_m)(S1​,…,Sm​) of connected labeled Veblen infragraphs whose edge counts sum to ddd. Then

ϕd(H)=∑m=1d(−(k−1)n)mm!∑(S1,…,Sm)∈Tm(d,H)∏i=1mCSi.\phi_d(\mathcal H)=\sum_{m=1}^{d}\frac{\bigl(-(k-1)^n\bigr)^m}{m!}\sum_{(S_1,\ldots,S_m)\in\mathscr T_m(d,\mathcal H)}\prod_{i=1}^{m}C_{S_i}.ϕd​(H)=m=1∑d​m!(−(k−1)n)m​(S1​,…,Sm​)∈Tm​(d,H)∑​i=1∏m​CSi​​.

The paper prints the equivalent sum over isomorphism classes, using automorphism groups and the copy weight (#H⊆H)(\#H\subseteq\mathcal H)(#H⊆H). The labeled expression is the result before that grouping. The isomorphism-class statement is outside this mission's Lean target and remains a separate formalization task.

Significance

The theorem expresses a coefficient of a resultant, defined algebraically over complex common roots, as a finite rational weighted sum over hypergraph configurations. It extends the graph case while preserving the exact normalization, sign, component count, and coefficient index. The formula can be used to study low codegree coefficients by enumerating finite Veblen infragraphs and their Euler rootings. Clark and Cooper also use associated coefficients to compare different hypergraph structures in the paper.

The formal development separates the resultant, the differential trace, and the combinatorial counts into reusable definitions. It would provide machine-checked statements of the paper's main equality and several links between those definitions. The Lean theorems in this draft are open targets with sorry placeholders; compiling their statements does not constitute a machine-checked proof of the paper's claims.

Difficulty

The ordinary matrix determinant has a direct expansion by permutations. A tensor characteristic polynomial is a multivariate resultant, so that expansion does not directly reveal which hypergraphs contribute to a coefficient. The trace side uses repeated partial derivatives, and repeated hyperedges can create several distinct rootings with the same directed arc multiplicities. Counting only a multiset of root assignments loses those orderings and changes the coefficient. The Eulerian condition, arborescence multiplicities, and factorial factors must agree simultaneously for the algebraic and combinatorial expressions to match.

Formalization scope

The Lean representation uses Fin n vertices, finite sets of simple edges, multisets of repeated edges, rational adjacency weights, and complex polynomials only for the resultant criterion. It assumes k≥2k\ge2k≥2, n≥1n\ge1n≥1, and 1≤d≤n(k−1)n−11\le d\le n(k-1)^{n-1}1≤d≤n(k−1)n−1. The source fixes k≥2k\ge2k≥2, and the latter two bounds expose the domain in which the printed coefficient expansion and its proof use ddd. An isolated vertex of H\mathcal HH counts in nnn and in (k−1)n(k-1)^n(k−1)n, while isolated vertices do not pad a multi-hypergraph component.

The formalization takes a polynomial satisfying the resultant's common-root, normalization, and irreducibility properties as an input. Separate sanity proofs give witnesses in the one-variable degree-zero and degree-one cases; degree one covers the first goal-domain degree when k=2k=2k=2. The characteristic polynomial is evaluated from this polynomial, never defined by the trace identity. Rootings are sequences, arborescences are directed toward the least positive-degree vertex, and the associated coefficient is calculated on covered vertices. The goal uses the labeled sum, so no automorphism is computed on an ambient padded vertex set. Contributions formalizing the omitted isomorphism-class equivalence, the BEST circuit count, and proofs of the stated targets are welcome.

Selected references

  • Gregory J. Clark and Joshua N. Cooper, A Harary-Sachs theorem for hypergraphs, Journal of Combinatorial Theory, Series B, 2021. arXiv:1812.00468v2; DOI 10.1016/j.jctb.2021.01.002.
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CombinatoricsGroup Theory·Captain: mikedeng1

Edge-Transitive Bi-Cayley Graphs 3: Every Connected Semisymmetric Bi-Dihedrant Has Valency at Least 6, and Semisymmetric Bi-Dihedrants of Valency 2k Exist for Each Odd k ≥ 3Research Paper

Motivation

A graph may have enough symmetry for every edge to look the same while its vertices still fall into two distinct classes. Such a graph is semisymmetric when it has constant valency, is edge-transitive, and is not vertex-transitive. Conder, Zhou, Feng and Zhang study this phenomenon among bi-Cayley graphs, graphs equipped with a group acting freely on two vertex orbits. Their dihedral case supplies examples related to questions of Marušič and Potočnik about semisymmetric tetracirculants. It also asks how small the valency of a semisymmetric graph with this specific group action can be. The paper states that six is the minimum and that examples occur at every valency 2k2k2k with odd k≥3k\ge3k≥3 (Conder et al., Theorem 1.5 and its introduction, p. 3).

The authors develop the bi-Cayley setting and the relevant automorphism actions before reaching the dihedral result. The lower bound appears as Theorem 6.1, while Example 6.2 defines a graph family and Proposition 6.4 gives a semisymmetry criterion for its odd-kkk members (Conder et al., pp. 11–13). This mission targets those claims within a single formal graph model. The final sentence of Theorem 1.5, concerning an edge-regular family, depends on the separate Theorem 6.5 and is outside this goal.

Setting

Let HHH be a finite group. Choose subsets R,L,S⊆HR,L,S\subseteq HR,L,S⊆H with R−1=RR^{-1}=RR−1=R, L−1=LL^{-1}=LL−1=L, 1∉R∪L1\notin R\cup L1∈/R∪L, and ∣R∣=∣L∣|R|=|L|∣R∣=∣L∣. The graph BiCay⁡(H,R,L,S)\operatorname{BiCay}(H,R,L,S)BiCay(H,R,L,S) has two labelled copies of each element of HHH, written h0h_0h0​ and h1h_1h1​. For each h∈Hh\in Hh∈H, it joins h0h_0h0​ to (xh)0(xh)_0(xh)0​ when x∈Rx\in Rx∈R, joins h1h_1h1​ to (yh)1(yh)_1(yh)1​ when y∈Ly\in Ly∈L, and joins h0h_0h0​ to (zh)1(zh)_1(zh)1​ when z∈Sz\in Sz∈S. These are unoriented edges. Right multiplication by HHH preserves the graph and acts regularly on each of its two vertex copies (Conder et al., pp. 1 and 4).

A bi-dihedrant is a bi-Cayley graph whose group is dihedral. Write Dn=⟨a,b∣an=b2=(ab)2=1⟩D_n=\langle a,b\mid a^n=b^2=(ab)^2=1\rangleDn​=⟨a,b∣an=b2=(ab)2=1⟩ for the dihedral group of degree n≥3n\ge3n≥3. Graph valency is the number of neighbours of a vertex. Connectedness is required throughout the paper. For a semisymmetric graph the common valency exists by definition, but the formal statements quantify over every vertex so that the numerical claims refer to the actual graph degree (Conder et al., definitions on p. 4 and Theorem 6.1 on p. 11).

The constructed family Γ(n,λ,2k)\Gamma(n,\lambda,2k)Γ(n,λ,2k) uses n≥5n\ge5n≥5, k≥2k\ge2k≥2, and a residue λ\lambdaλ of multiplicative order 2k2k2k modulo nnn whose even-power sum vanishes: ∑j=0k−1λ2j=0\sum_{j=0}^{k-1}\lambda^{2j}=0∑j=0k−1​λ2j=0. For 0≤i<k0\le i<k0≤i<k, put ci=∑j=0iλ2jc_i=\sum_{j=0}^{i}\lambda^{2j}ci​=∑j=0i​λ2j and di=λcid_i=\lambda c_idi​=λci​. Set S(n,λ,2k)={aci:0≤i<k}∪{badi:0≤i<k}S(n,\lambda,2k)=\{a^{c_i}:0\le i<k\}\cup\{ba^{d_i}:0\le i<k\}S(n,λ,2k)={aci​:0≤i<k}∪{badi​:0≤i<k}. Then Γ(n,λ,2k)=BiCay⁡(Dn,∅,∅,S(n,λ,2k))\Gamma(n,\lambda,2k)=\operatorname{BiCay}(D_n,\varnothing,\varnothing,S(n,\lambda,2k))Γ(n,λ,2k)=BiCay(Dn​,∅,∅,S(n,λ,2k)) (Conder et al., Example 6.2, pp. 12–13).

Right multiplication R(g):hi↦(hg)iR(g):h_i\mapsto(hg)_iR(g):hi​↦(hg)i​ gives a subgroup R(H)≤Aut⁡(Γ)R(H)\le\operatorname{Aut}(\Gamma)R(H)≤Aut(Γ), and X=NAut⁡(Γ)(R(H))X=N_{\operatorname{Aut}(\Gamma)}(R(H))X=NAut(Γ)​(R(H)) is its normaliser. For α∈Aut⁡(H)\alpha\in\operatorname{Aut}(H)α∈Aut(H) and g,x,y∈Hg,x,y\in Hg,x,y∈H the paper's permutations are σα,g:h0↦(hα)0, h1↦(ghα)1\sigma_{\alpha,g}:h_0\mapsto(h^\alpha)_0,\ h_1\mapsto(gh^\alpha)_1σα,g​:h0​↦(hα)0​, h1​↦(ghα)1​ and δα,x,y:h0↦(xhα)1, h1↦(yhα)0\delta_{\alpha,x,y}:h_0\mapsto(xh^\alpha)_1,\ h_1\mapsto(yh^\alpha)_0δα,x,y​:h0​↦(xhα)1​, h1​↦(yhα)0​; the sets F\mathrm FF and I\mathrm II of (2.2) collect those that preserve the edge rules. A graph is normal edge-transitive when XXX, not merely Aut⁡(Γ)\operatorname{Aut}(\Gamma)Aut(Γ), is transitive on edges (Conder et al., pp. 4–5).

Formalization targets

The milestones, in attack order, are Proposition 2.1(b) (one may assume 1∈S1\in S1∈S), Proposition 2.2 (the normaliser X=R(H)(F∪I)X=R(H)(\mathrm F\cup\mathrm I)X=R(H)(F∪I)), the opening step of the proof of Theorem 6.1 (R=L=∅R=L=\varnothingR=L=∅ for a semisymmetric graph), Theorem 6.1, the claims of Example 6.2, and Proposition 6.4.

Minimum valency

For every connected semisymmetric bi-dihedrant over DnD_nDn​, n≥3n\ge3n≥3, and every vertex vvv,

deg⁡Γ(v)≥6.\deg_{\Gamma}(v)\ge6.degΓ​(v)≥6.

This is Theorem 6.1 and the first claim of Theorem 1.5. The milestone for the proof's opening step records that semisymmetry forces R=L=∅R=L=\varnothingR=L=∅; the theorem itself still ranges over all valid R,L,SR,L,SR,L,S (Conder et al., Theorem 6.1, p. 11).

Examples at every odd index

For each odd integer k≥3k\ge3k≥3, there is a connected semisymmetric bi-dihedrant Γ\GammaΓ such that

deg⁡Γ(v)=2kfor every vertex v.\deg_{\Gamma}(v)=2k\qquad\text{for every vertex }v.degΓ​(v)=2kfor every vertex v.

Example 6.2 is the first milestone toward this claim: under its hypotheses Γ(n,λ,2k)\Gamma(n,\lambda,2k)Γ(n,λ,2k) is connected, ∣S∣=2k|S|=2k∣S∣=2k, every vertex has degree 2k2k2k, and σα,b\sigma_{\alpha,b}σα,b​ for α:(a,b)↦(aλ,ba)\alpha:(a,b)\mapsto(a^\lambda,ba)α:(a,b)↦(aλ,ba) lies in XXX and permutes the neighbours of 101_010​ cyclically, so the graph is normal edge-transitive (Conder et al., p. 13). Proposition 6.4 is the second: under the defining conditions for Γ(n,λ,2k)\Gamma(n,\lambda,2k)Γ(n,λ,2k), odd kkk and λk=−1\lambda^k=-1λk=−1 modulo nnn imply semisymmetry (Conder et al., Proposition 6.4, p. 13). An auxiliary arithmetic theorem states the parameter existence needed to apply it for every odd k≥3k\ge3k≥3.

Significance

The lower bound excludes semisymmetric bi-dihedrants of valency at most five. The existence assertion says that six is attained and places examples at an infinite sequence of larger valencies. Together these claims distinguish the restrictions imposed by a dihedral bi-Cayley action from edge transitivity alone. The construction also gives a concrete family on which questions about stronger automorphism properties can be posed (Conder et al., §6, pp. 11–14).

The result is proved in the cited paper; this mission seeks a machine-checked version of its first two Theorem 1.5 claims. The definition of a bi-Cayley graph, the precise graph symmetry predicates, and the arithmetic parameter family are reusable beyond this particular lower bound. The statements here are open proof targets in Lean, with their mathematical content fixed before proof development. The edge-regular claim from the theorem's final sentence requires additional source results and remains a separate formalization target.

Difficulty

Edge transitivity alone does not make a graph vertex-transitive: a semisymmetric graph is precisely the regular case where that inference fails. The lower bound therefore cannot be obtained by treating every vertex as lying in one automorphism orbit. In the dihedral case, the proof must account for both the rotations and reflections in the connection set, and for the constraints that connectedness and graph automorphisms impose on low-valency possibilities (Conder et al., proof of Theorem 6.1, pp. 11–12).

The existence statement has a distinct arithmetic requirement. Proposition 6.4 is conditional on n,λ,kn,\lambda,kn,λ,k satisfying Example 6.2 and λk=−1\lambda^k=-1λk=−1. The paper asserts examples for every odd k≥3k\ge3k≥3 without spelling out parameters for all such kkk. The auxiliary arithmetic statement records that remaining obligation explicitly; a proof of the conditional proposition alone would not establish the quantified existence claim.

Formalization scope

Lean represents the two vertex copies by H ⊕ H and the dihedral group by Mathlib's DihedralGroup n. The graph is a SimpleGraph built from the three edge rules above, with the inverse-closure and loop-exclusion conditions retained in BiCayData. Semisymmetry uses the full graph automorphism group, unoriented edge transitivity, and constant graph degree. The source's standing finite, connected, simple, and undirected conventions are explicit through finite groups, simple graphs, and connectedness hypotheses. The goal and Theorem 6.1 use n≥3n\ge3n≥3, the range of dihedral groups in §6; the existence examples are also required to be connected.

The definitions do not set valency equal to ∣S∣|S|∣S∣, replace a graph degree by a formula, or make an edge-transitivity claim true by ranging over an empty edge set. The setting requires valid inverse-closed side sets rather than silently symmetrising an invalid one. The arithmetic hypothesis uses the multiplicative order in Z/nZ\mathbb Z/n\mathbb ZZ/nZ and enumerates the indices of Zk\mathbb Z_kZk​ by 0,…,k−10,\ldots,k-10,…,k−1. Normal edge-transitivity in Example 6.2 is stated for the subgroup XXX and semisymmetry for the full group Aut⁡(Γ)\operatorname{Aut}(\Gamma)Aut(Γ); the two are not interchanged. The restriction n≥3n\ge3n≥3 in the lower bound is that of Theorem 6.1 and is a disclosed hypothesis of the goal: D1D_1D1​ and D2D_2D2​ are abelian, and the paper treats bi-Cayley graphs over abelian groups separately. The auxiliary parameter statement is not in the paper; it is supplied so that the existence claim has a complete proof path. The third sentence of Theorem 1.5 is not formalized. Contributions establishing the structural lower bound, the family criterion, or the arithmetic existence statement are all within scope.

Selected references

  • Marston Conder, Jin-Xin Zhou, Yan-Quan Feng and Mi-Mi Zhang, Edge-transitive bi-Cayley graphs, preprint arXiv:1606.04625v1, 2016; published in Journal of Combinatorial Theory, Series B, 2020. Preprint · DOI.
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CombinatoricsGroup Theory·Captain: mikedeng1

Edge-Transitive Bi-Cayley Graphs 1: For Connected BiCay(H, ∅, ∅, S), the Normaliser of R(H) Is 2-Arc-Transitive Iff Three Conditions on Aut(H) Hold, and Never 3-Arc-TransitiveResearch Paper

Motivation

A graph is symmetric when its automorphism group moves any vertex, edge or arc to any other, and sss-arc-transitive when it moves any walk of length sss without immediate backtracking to any other. Tutte's work on cubic graphs made sss-arc-transitivity a central measure of symmetry in algebraic graph theory, and most constructions of highly symmetric graphs start from a group acting regularly on vertices (Cayley graphs) or semiregularly with a few orbits.

Bi-Cayley graphs are the case of two orbits: a group HHH acts semiregularly by right multiplication with exactly two vertex orbits. The Petersen graph, the Gray graph (the smallest cubic semisymmetric graph) and the smallest member of Bouwer's family of half-arc-transitive graphs are all bi-Cayley graphs, and all of them are edge-transitive. Deciding edge-transitivity of a bi-Cayley graph is hard in general, but the subgroup of automorphisms that normalise the semiregular group R(H)R(H)R(H) is explicitly known, which makes the "normal" version of the question tractable.

C. H. Li asked whether 3-arc-transitive bi-normal Cayley graphs exist and asked for a description of the 2-arc-transitive ones (Li, Proc. Amer. Math. Soc. 133, Question 1.2(a) and Problem 1.3(b), as cited in the paper, p. 2). M. Conder, J.-X. Zhou, Y.-Q. Feng and M.-M. Zhang answered both through their Theorem 1.1 (arXiv:1606.04625v1; J. Combin. Theory Ser. B, 2020). This mission formalizes that theorem and the lemmas its proof uses.

Setting

Let HHH be a finite group with identity 111. Let R,L,S⊆HR, L, S \subseteq HR,L,S⊆H with ∣R∣=∣L∣|R| = |L|∣R∣=∣L∣, 1∉R=R−11 \notin R = R^{-1}1∈/R=R−1 and 1∉L=L−11 \notin L = L^{-1}1∈/L=L−1. The bi-Cayley graph Γ=BiCay(H,R,L,S)\Gamma = \mathrm{BiCay}(H,R,L,S)Γ=BiCay(H,R,L,S) has vertex set H0∪H1H_0 \cup H_1H0​∪H1​, two disjoint copies of HHH (the copy of hhh in HiH_iHi​ is hih_ihi​), and edges {h0,(xh)0}\{h_0,(xh)_0\}{h0​,(xh)0​}, {h1,(yh)1}\{h_1,(yh)_1\}{h1​,(yh)1​} and {h0,(zh)1}\{h_0,(zh)_1\}{h0​,(zh)1​} for h∈Hh \in Hh∈H, x∈Rx \in Rx∈R, y∈Ly \in Ly∈L, z∈Sz \in Sz∈S. The main theorem concerns R=L=∅R = L = \emptysetR=L=∅, where Γ\GammaΓ is bipartite with parts H0H_0H0​, H1H_1H1​ and every vertex has ∣S∣|S|∣S∣ neighbours.

For g∈Hg \in Hg∈H, R(g):hi↦(hg)iR(g) : h_i \mapsto (hg)_iR(g):hi​↦(hg)i​ is an automorphism of Γ\GammaΓ; these form the subgroup R(H)≤Aut(Γ)R(H) \le \mathrm{Aut}(\Gamma)R(H)≤Aut(Γ), and

X=NAut(Γ)(R(H))X = N_{\mathrm{Aut}(\Gamma)}(R(H))X=NAut(Γ)​(R(H))

is its normaliser. For α∈Aut(H)\alpha \in \mathrm{Aut}(H)α∈Aut(H) (written as an exponent, h↦hαh \mapsto h^\alphah↦hα) and x,y,g∈Hx, y, g \in Hx,y,g∈H the paper defines the permutations

δα,x,y:h0↦(xhα)1, h1↦(yhα)0,σα,g:h0↦(hα)0, h1↦(ghα)1,\delta_{\alpha,x,y} : h_0 \mapsto (xh^\alpha)_1,\ h_1 \mapsto (yh^\alpha)_0, \qquad \sigma_{\alpha,g} : h_0 \mapsto (h^\alpha)_0,\ h_1 \mapsto (gh^\alpha)_1,δα,x,y​:h0​↦(xhα)1​, h1​↦(yhα)0​,σα,g​:h0​↦(hα)0​, h1​↦(ghα)1​,

and the sets I\mathrm{I}I of those δα,x,y\delta_{\alpha,x,y}δα,x,y​ with Rα=x−1LxR^\alpha = x^{-1}LxRα=x−1Lx, Lα=y−1RyL^\alpha = y^{-1}RyLα=y−1Ry, Sα=y−1S−1xS^\alpha = y^{-1}S^{-1}xSα=y−1S−1x, and F\mathrm{F}F of those σα,g\sigma_{\alpha,g}σα,g​ with Rα=RR^\alpha = RRα=R, Lα=g−1LgL^\alpha = g^{-1}LgLα=g−1Lg, Sα=g−1SS^\alpha = g^{-1}SSα=g−1S.

An sss-arc is a sequence (v0,…,vs)(v_0,\dots,v_s)(v0​,…,vs​) of vertices in which consecutive vertices are adjacent and vi−1≠vi+1v_{i-1} \ne v_{i+1}vi−1​=vi+1​. A subgroup K≤Aut(Γ)K \le \mathrm{Aut}(\Gamma)K≤Aut(Γ) is transitive on sss-arcs if any sss-arc can be mapped to any other by an element of KKK; 1-arcs are arcs. Γ\GammaΓ is normal edge-transitive if XXX is transitive on edges, and normal locally arc-transitive if the stabiliser X10X_{1_0}X10​​ is transitive on the neighbourhood Γ(10)\Gamma(1_0)Γ(10​). KKK acts semisymmetrically if it is edge-transitive but not vertex-transitive. Aut(H,Δ)\mathrm{Aut}(H,\Delta)Aut(H,Δ) is the setwise stabiliser of Δ⊆H\Delta \subseteq HΔ⊆H in Aut(H)\mathrm{Aut}(H)Aut(H).

Formalization targets

Goal: Theorem 1.1

Let Γ=BiCay(H,∅,∅,S)\Gamma = \mathrm{BiCay}(H,\emptyset,\emptyset,S)Γ=BiCay(H,∅,∅,S) be connected, with 1∈S1 \in S1∈S and ∣S∣≥2|S| \ge 2∣S∣≥2. Then

X is transitive on the 2-arcs of Γ  ⟺  (a)∧(b)∧(c),X \text{ is transitive on the 2-arcs of } \Gamma \iff \text{(a)} \wedge \text{(b)} \wedge \text{(c)},X is transitive on the 2-arcs of Γ⟺(a)∧(b)∧(c),

where (a) Sα=S−1S^\alpha = S^{-1}Sα=S−1 for some α∈Aut(H)\alpha \in \mathrm{Aut}(H)α∈Aut(H); (b) Aut(H,S∖{1})\mathrm{Aut}(H, S\setminus\{1\})Aut(H,S∖{1}) is transitive on S∖{1}S \setminus \{1\}S∖{1}; (c) Sβ=s−1SS^\beta = s^{-1}SSβ=s−1S for some s∈S∖{1}s \in S\setminus\{1\}s∈S∖{1} and β∈Aut(H)\beta \in \mathrm{Aut}(H)β∈Aut(H). Furthermore, if ∣S∣≥3|S| \ge 3∣S∣≥3, XXX is not transitive on the 3-arcs of Γ\GammaΓ.

Milestones

  • Proposition 2.2 (cited by the paper from Zhou and Feng): for connected Γ\GammaΓ, every element of F\mathrm{F}F and of I\mathrm{I}I is an automorphism in XXX, and X=R(H) (F∪I)X = R(H)\,(\mathrm{F} \cup \mathrm{I})X=R(H)(F∪I).
  • Proposition 2.2(a): for δα,x,y∈I\delta_{\alpha,x,y} \in \mathrm{I}δα,x,y​∈I, ⟨R(H),δα,x,y⟩\langle R(H),\delta_{\alpha,x,y}\rangle⟨R(H),δα,x,y​⟩ is vertex-transitive.
  • Lemma 3.2: X1011={σα,1∣α∈Aut(H,S∖{1})}X_{1_01_1} = \{\sigma_{\alpha,1} \mid \alpha \in \mathrm{Aut}(H,S\setminus\{1\})\}X10​11​​={σα,1​∣α∈Aut(H,S∖{1})}, and, for ∣S∣≥3|S| \ge 3∣S∣≥3, XXX is not 3-arc-transitive.
  • Proposition 3.3: Γ\GammaΓ is normal locally arc-transitive iff Γ(10)\Gamma(1_0)Γ(10​) is an orbit of F\mathrm{F}F; in that case XXX is arc-transitive iff Sα=S−1S^\alpha = S^{-1}Sα=S−1 for some α\alphaα, and semisymmetric otherwise.

A companion item, Lemma 3.1, shows that a connected normal edge-transitive BiCay(H,R,L,S)\mathrm{BiCay}(H,R,L,S)BiCay(H,R,L,S) has R=L=∅R = L = \emptysetR=L=∅; it explains why the goal is stated for BiCay(H,∅,∅,S)\mathrm{BiCay}(H,\emptyset,\emptyset,S)BiCay(H,∅,∅,S).

Significance

Combined with Lemma 3.1, Theorem 1.1 reduces 2-arc-transitivity of the normaliser of R(H)R(H)R(H) to three conditions on Aut(H)\mathrm{Aut}(H)Aut(H) and the single set SSS, all checkable in the group without building the graph. Its second part rules out normal 3-arc-transitive bi-Cayley graphs of valency at least 3, which settles Li's question on 3-arc-transitive bi-normal Cayley graphs negatively. Proposition 3.3 is the arc-transitive versus semisymmetric dichotomy for normal locally arc-transitive bi-Cayley graphs, the starting point of the paper's later constructions of semisymmetric and half-arc-transitive examples.

All results are proved in the paper (Proposition 2.2 by citation). None of them has a machine-checked proof that this mission knows of. Mathlib has simple graphs, graph isomorphisms and normalisers, but no bi-Cayley graphs, no sss-arcs and no transitivity notions for subgroups of Aut(Γ)\mathrm{Aut}(\Gamma)Aut(Γ). A formal proof would supply the first verified description of the normaliser of a semiregular group with two orbits, reusable for any bi-Cayley or bi-circulant result.

Difficulty

The bulk of the work is Proposition 2.2. That σα,g\sigma_{\alpha,g}σα,g​ and δα,x,y\delta_{\alpha,x,y}δα,x,y​ preserve edges exactly under the conditions of (2.2) is a direct computation. The converse is harder: every automorphism normalising R(H)R(H)R(H) must be one of these permutations composed with some R(h)R(h)R(h). That needs the conjugation action of XXX on R(H)R(H)R(H) to induce an automorphism of HHH, and XXX to preserve the partition {H0,H1}\{H_0, H_1\}{H0​,H1​} into R(H)R(H)R(H)-orbits.

The 3-arc clause looks like a stabiliser count, but it holds only once 111_111​ has at least two neighbours besides 101_010​: for ∣S∣=2|S| = 2∣S∣=2 the graph is a cycle of length 2∣H∣2|H|2∣H∣, the normaliser is the whole dihedral automorphism group, and that group is transitive on 3-arcs. Any proof must use ∣S∣≥3|S| \ge 3∣S∣≥3 at exactly this step.

Formalization scope

The vertex set is H ⊕ H (Sum.inl h =h0= h_0=h0​, Sum.inr h =h1= h_1=h1​). The data (R,L,S)(R, L, S)(R,L,S) is a structure of three Finsets carrying the side conditions R−1=RR^{-1} = RR−1=R, L−1=LL^{-1} = LL−1=L, 1∉R1 \notin R1∈/R, 1∉L1 \notin L1∈/L and ∣R∣=∣L∣|R| = |L|∣R∣=∣L∣. Adjacency is given directly by the three edge rules. Aut(Γ)\mathrm{Aut}(\Gamma)Aut(Γ) is the group Γ ≃g Γ, R(H)R(H)R(H) is the subgroup generated by the R(g)R(g)R(g), and XXX is its Subgroup.normalizer. Automorphisms of HHH are MulAut H, and Δα\Delta^\alphaΔα is the image of Δ\DeltaΔ under α\alphaα. σα,g\sigma_{\alpha,g}σα,g​ and δα,x,y\delta_{\alpha,x,y}δα,x,y​ are permutations of H ⊕ H, and "σα,g∈X\sigma_{\alpha,g} \in Xσα,g​∈X" means that some element of XXX has σα,g\sigma_{\alpha,g}σα,g​ as its vertex map. Products are written pointwise to avoid the composition-order convention of the group of graph automorphisms. An sss-arc is a function on Fin (s+1).

The paper assumes throughout that groups and graphs are finite and graphs connected; each theorem carries Fintype H and connectivity. Three hypotheses are added to the printed Theorem 1.1, each needed for the statement to be true or non-vacuous: 1∈S1 \in S1∈S (the paper's normalisation of a bi-Cayley triple, used by conditions (b) and (c) and by the proof); ∣S∣≥2|S| \ge 2∣S∣≥2 (for ∣S∣=1|S| = 1∣S∣=1 the graph is K2K_2K2​, which has no 2-arcs, so 2-arc-transitivity would hold vacuously while (c) fails); and ∣S∣≥3|S| \ge 3∣S∣≥3 for the 3-arc clause (false for cycles). Lemma 3.2 carries the same 1∈S1 \in S1∈S and ∣S∣≥3|S| \ge 3∣S∣≥3, and Proposition 3.3 carries 1∈S1 \in S1∈S.

Trivializing formalizations are ruled out in four ways. 2-arc-transitivity is never vacuous, since ∣S∣≥2|S| \ge 2∣S∣≥2 guarantees 2-arcs. RRR and LLL are never symmetrised silently, since the side conditions are data rather than repairs. Every transitivity statement is about XXX, not the full Aut(Γ)\mathrm{Aut}(\Gamma)Aut(Γ). Valency is the degree in the graph, never a formula in ∣S∣|S|∣S∣.

Contributions are welcome at every level: proofs of the milestones, a general library of bi-Cayley graphs and their normalisers, and lemmas on sss-arcs and stabilisers in SimpleGraph. The definitions are repeated identically in the companion missions on bi-abelian graphs and bi-dihedrants.

Selected references

  • M. Conder, J.-X. Zhou, Y.-Q. Feng and M.-M. Zhang, Edge-transitive bi-Cayley graphs, arXiv:1606.04625v1, 2016; J. Combin. Theory Ser. B, 2020. https://arxiv.org/abs/1606.04625v1, https://doi.org/10.1016/j.jctb.2020.05.006
  • J.-X. Zhou and Y.-Q. Feng, The automorphisms of bi-Cayley graphs, J. Combin. Theory Ser. B 116 (2016) 504–532 (reference [55] of the paper; source of Proposition 2.2). https://doi.org/10.1016/j.jctb.2015.10.004
  • C. H. Li, Finite s-arc transitive Cayley graphs and flag transitive projective planes, Proc. Amer. Math. Soc. 133 (2005) 31–41 (reference [29] of the paper; Question 1.2(a) and Problem 1.3(b)). https://doi.org/10.1090/S0002-9939-04-07549-5
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CombinatoricsGroup Theory·Captain: mikedeng1

Edge-Transitive Bi-Cayley Graphs 2: Edge-Transitive Bi-Cayley Graphs over Abelian Groups Are Vertex-Transitive, and Half-Arc-Transitive Ones Have Valency at Least 6Research Paper

Why bi-Cayley graphs over abelian groups matter

A Cayley graph turns multiplication in a group into adjacency between vertices. Every Cayley graph is vertex transitive: multiplying every vertex by the same group element moves any chosen vertex to any other. A bi-Cayley graph starts with two copies of a group and permits edges both within and between the copies. Its built-in group action is transitive on each copy, but it need not exchange them. This makes bi-Cayley graphs a setting for distinguishing edge, vertex, and arc transitivity. Conder, Zhou, Feng and Zhang study these distinctions and their consequences for graph constructions in Edge-transitive bi-Cayley graphs.

For abelian groups, the paper proves a strong restriction: once a connected bi-Cayley graph is edge transitive, it is also vertex transitive. Its half-arc-transitive examples face additional local restrictions, including a minimum valency of six. The paper exhibits a graph of valency six in Example 4.3, so the numerical bound is attained in its class. The result separates these graphs from semisymmetric graphs, which are edge transitive but not vertex transitive. The claims and example appear in Proposition 1.3 and §4.

Graphs, actions, and terminology

Let HHH be a finite group and let R,L,SR,L,SR,L,S be subsets of HHH. Require R=R−1R=R^{-1}R=R−1, L=L−1L=L^{-1}L=L−1, 1∉R∪L1\notin R\cup L1∈/R∪L, and ∣R∣=∣L∣|R|=|L|∣R∣=∣L∣. The graph Γ=BiCay⁡(H,R,L,S)\Gamma=\operatorname{BiCay}(H,R,L,S)Γ=BiCay(H,R,L,S) has vertices H0∪H1H_0\cup H_1H0​∪H1​, two labelled copies of HHH. For h∈Hh\in Hh∈H, an element x∈Rx\in Rx∈R gives an edge from h0h_0h0​ to (xh)0(xh)_0(xh)0​; an element y∈Ly\in Ly∈L gives an edge from h1h_1h1​ to (yh)1(yh)_1(yh)1​; and an element z∈Sz\in Sz∈S gives an edge from h0h_0h0​ to (zh)1(zh)_1(zh)1​. The inverse closure of RRR and LLL makes the within-copy edges undirected. Excluding 111 from those two sets excludes loops. This is the construction on p. 1.

Every g∈Hg\in Hg∈H acts by right multiplication R(g):hi↦(hg)iR(g):h_i\mapsto(hg)_iR(g):hi​↦(hg)i​. The subgroup R(H)R(H)R(H) of graph automorphisms has two vertex orbits, H0H_0H0​ and H1H_1H1​. Its normaliser in Aut⁡(Γ)\operatorname{Aut}(\Gamma)Aut(Γ) is useful for describing further symmetries. For a group automorphism α\alphaα and group elements g,x,yg,x,yg,x,y, the paper defines permutations σα,g\sigma_{\alpha,g}σα,g​, which preserve the two copies, and δα,x,y\delta_{\alpha,x,y}δα,x,y​, which exchange them. Their precise edge-preservation conditions are the sets FFF and III of equation (2.2). The mission includes those definitions and the relevant part of the normaliser characterization, Proposition 2.2.

A graph is vertex transitive if its full automorphism group can move any vertex to any other, and edge transitive if it can move any undirected edge to any other. Arc transitivity asks the same for ordered adjacent pairs. A graph is half-arc-transitive when it is vertex and edge transitive but not arc transitive. A semisymmetric graph has constant valency, is edge transitive, and is not vertex transitive. These are the paper's definitions on p. 4. Valency means the actual number of neighbours of a vertex.

Formalization targets

The central target is the corrected reading of Proposition 1.3: for connected Γ\GammaΓ over an abelian HHH,

Γ edge transitive  ⟹  Γ vertex transitive,\Gamma\text{ edge transitive}\implies\Gamma\text{ vertex transitive},Γ edge transitive⟹Γ vertex transitive,

and, if Γ\GammaΓ is half-arc-transitive,

R∪L≠∅,(∀x∈R∪L) ord⁡(x)≠2,∣R∣=∣L∣ is even,∣S∣>2,deg⁡Γ(v)≥6(v∈V(Γ)).R\cup L\ne\varnothing,\quad (\forall x\in R\cup L)\ \operatorname{ord}(x)\ne2, \quad |R|=|L|\text{ is even},\quad |S|>2,\quad \deg_{\Gamma}(v)\ge6\quad(v\in V(\Gamma)).R∪L=∅,(∀x∈R∪L) ord(x)=2,∣R∣=∣L∣ is even,∣S∣>2,degΓ​(v)≥6(v∈V(Γ)).

The attack path records the normaliser characterization in Proposition 2.2, both parts of Proposition 4.1, its explicit bipartition step, and Proposition 4.2. The companion Corollary 4.4 expresses the absence of connected semisymmetric examples over abelian groups.

What the result establishes

The vertex-transitivity conclusion collapses one apparent possibility in the symmetry classification: an edge-transitive bi-Cayley graph over an abelian group cannot be semisymmetric. For half-arc-transitive graphs, the theorem rules out involutions in the within-copy connection sets, forces even set sizes, and excludes valencies below six. Example 4.3 supplies an attained bound, with H=C28H=C_{28}H=C28​ and valency six. These are concrete restrictions on any later classification or construction in this family; they follow from §4.

The mathematical results are proved in the cited paper. The mission asks for machine-checked Lean proofs of their statements and for a reusable finite bi-Cayley graph interface. The setting includes the right regular action, its normaliser, the two families of candidate automorphisms, and the transitivity predicates. The proof obligations are open in this local proposal; a successful development would make the abelian argument and its underlying graph-action lemmas available for later formalizations of bi-Cayley graphs.

The main obstacle

The right multiplication action gives only two known vertex orbits, one for each copy of HHH. Edge transitivity alone does not name an automorphism that swaps them. Inversion is an automorphism of an abelian group, but its induced copy-swapping permutation preserves graph edges directly only in the relevant bipartite case R=L=∅R=L=\varnothingR=L=∅. The paper must account for edge-transitive graphs whose connection sets initially allow edges inside the copies. For the valency bound, the absence of arc transitivity restricts how a vertex stabiliser can permute its neighbours; degree counting without those orbit constraints cannot yield ∣S∣>2|S|>2∣S∣>2. These are the points at which a proof must use the full automorphism action rather than just the visible right regular subgroup.

Formalization scope

Lean uses the vertex type H⊔HH\sqcup HH⊔H, with Sum.inl h for h0h_0h0​ and Sum.inr h for h1h_1h1​. The group and graph are finite; the graph is simple and undirected by construction, and every theorem assumes it is connected. The finite sets R,L,SR,L,SR,L,S retain the conditions R=R−1R=R^{-1}R=R−1, L=L−1L=L^{-1}L=L−1, 1∉R∪L1\notin R\cup L1∈/R∪L, and ∣R∣=∣L∣|R|=|L|∣R∣=∣L∣. The graph's degree, rather than a separately defined cardinality formula, represents valency. “Involution” means an element of order exactly two. All graph-level transitivity predicates quantify over the full automorphism group; the normaliser appears only in the separate Proposition 2.2 milestone. The paper's exponent notation for group automorphisms is rendered as function application, while R(g)R(g)R(g) remains right multiplication.

The printed first sentence of Proposition 1.3 says every connected bi-Cayley graph over an abelian group is vertex transitive. That sentence is false: the connected generalized Petersen graph GP(7,2)GP(7,2)GP(7,2) is a bi-Cayley graph over C7C_7C7​ and is not vertex transitive. Proposition 4.1(b), which the paper uses to establish Proposition 1.3, includes the missing edge-transitivity condition. The Lean goal states that corrected condition explicitly. It does not add an assumption to the half-arc-transitive clause, since half-arc-transitivity already entails edge transitivity. Proposition 4.2 prints two clauses labelled “(a)”; they are treated as distinct clauses while its quotation retains the printed labels.

The definition of the graph keeps RRR and LLL inverse closed as data; no graph constructor silently symmetrises arbitrary sets. Transitivity is taken over actual edges or arcs, and the degree bound is checked at every vertex. Contributions to the graph-action lemmas, the normaliser result, Proposition 4.1, and the vertex-stabiliser analysis behind Proposition 4.2 are all within scope. The Cayley-graph isomorphism in Proposition 2.2(b) is outside this mission's stated milestone because it is not used by the target.

Selected references

  • M. Conder, J.-X. Zhou, Y.-Q. Feng, and M.-M. Zhang, Edge-transitive bi-Cayley graphs, Journal of Combinatorial Theory, Series B, 2020; arXiv:1606.04625v1; DOI:10.1016/j.jctb.2020.05.006.
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CombinatoricsTheoretical Computer Science·Captain: mikedeng1

Twin-width I: Tractable FO Model Checking 4: Every K_t-Minor-Free Graph Has Twin-width at Most 4c_g(t)·2^(4c_g(t)+2), a Triple-Exponential Bound in tResearch Paper

Why excluded minors matter here

Many graph classes are defined by excluding a fixed graph as a minor. A minor can be formed by deleting vertices, deleting edges, and contracting edges. Planar graphs, for example, exclude K5K_5K5​ as a minor. Édouard Bonnet, Eun Jung Kim, Stéphan Thomassé, and Rémi Watrigant proved that every class excluding a fixed clique minor has bounded twin-width, a graph parameter that controls how much adjacency information is lost while vertices are successively combined. Their result connects a classical graph-minor restriction with the contraction parameter used in their first-order model-checking framework. The explicit bound is very large, but it establishes that the width depends only on the excluded clique size, rather than on the graph's number of vertices. Bonnet et al., Twin-width I, Theorem 6.3.

Graphs, contractions, and matrix orders

Let GGG be a finite simple graph. A partition of its vertices is a collection of nonempty, disjoint parts covering all vertices. Two distinct parts are homogeneous if every vertex pair across them is adjacent, or every such pair is nonadjacent. Otherwise the pair of parts is marked red. A ddd-partition has at most ddd red neighbours at every part. A contraction sequence begins with singleton parts, merges two parts at each step, and finishes with at most one part. The statement tww⁡(G)≤d\operatorname{tww}(G)\le dtww(G)≤d says that such a sequence exists with every intermediate partition a ddd-partition. This partition formulation is equivalent to the paper's original description in terms of trigraph contractions. Bonnet et al., pp. 3:11–3:12, 3:32.

Write KtK_tKt​ for the complete graph on ttt vertices. The graph GGG is KtK_tKt​-minor free when KtK_tKt​ cannot be obtained from GGG by the minor operations above. The Lean statement uses a branch-set formulation: a minor model consists of disjoint, nonempty, connected vertex sets, with a connecting edge for each edge of the minor. Bonnet et al., p. 3:9.

An ordering σ\sigmaσ of the vertices gives a Boolean adjacency matrix Aσ(G)A_\sigma(G)Aσ​(G), with entry 111 at an edge and 000 otherwise. A division cuts its rows and columns into nonempty consecutive intervals. A zone is mixed if it is neither constant down its columns nor constant across its rows. A kkk-mixed minor is a division with kkk row intervals and kkk column intervals in which every zone is mixed; a kkk-grid minor requires a 111 in every zone. A matrix is kkk-mixed free or kkk-grid free if the corresponding minor does not exist. A mixed Boolean zone always contains a 111, so grid freeness implies mixed freeness. Bonnet et al., pp. 3:18–3:19.

The order used in the proof is a depth-first search (DFS). A discovery order v1,…,vnv_1,\dots,v_nv1​,…,vn​ lists the vertices; at each time the active vertex is the lastly discovered vertex that still has an undiscovered neighbour, and the next vertex is a neighbour of the active vertex, which becomes its parent in the DFS tree T\mathcal TT. The Lex-DFS breaks ties: among the connected components of the undiscovered vertices that meet the active vertex's neighbourhood, it enters one whose word (the 0/10/10/1 vector recording which discovered vertices have a neighbour in the component) is lexicographically largest. The proof also uses minimal subtrees of T\mathcal TT: the union of the tree paths between the vertices of a set. Bonnet et al., pp. 3:26–3:27.

Formalization targets

The main target is the explicit excluded-clique-minor bound. With

ck=83(k+1)224k,g(t)=2(24t+1+2)2,f(t)=4cg(t)24cg(t)+2,c_k=\frac83(k+1)^2 2^{4k},\qquad g(t)=2(2^{4t+1}+2)^2,\qquad f(t)=4c_{g(t)}2^{4c_{g(t)}+2},ck​=38​(k+1)224k,g(t)=2(24t+1+2)2,f(t)=4cg(t)​24cg(t)​+2,

the goal says

Kt⪯̸G⟹tww⁡(G)≤f(t).K_t\not\preceq G\quad\Longrightarrow\quad \operatorname{tww}(G)\le f(t).Kt​⪯G⟹tww(G)≤f(t).

The +2+2+2 in g(t)g(t)g(t) follows the value defined and used in the proof of Theorem 6.3 on p. 3:26; the printed theorem statement has +1+1+1. The formal target uses the proof-supported bound and records the printed statement verbatim in its provenance. It does not assert the paper's asymptotic triple-exponential notation as a separate theorem. Bonnet et al., pp. 3:26–3:29.

The milestones follow the proof in order:

  1. twin-width is the maximum over connected components;
  2. a Lex-DFS exists from any vertex of a connected graph;
  3. Lemma 6.4: in any DFS, the vertices ai,ja_{i,j}ai,j​ of the proof lie on one branch of T\mathcal TT;
  4. Lemma 6.5: the enhancements Bj∗B^*_jBj∗​ avoid the paths Ai′A'_iAi′​, except the last ones;
  5. ∣V(H)∣≤α(H)ω(H)|V(H)|\le\alpha(H)\omega(H)∣V(H)∣≤α(H)ω(H) for intersection graphs of subtrees of a tree;
  6. the Helly property for subtrees of a tree;
  7. Lemma 6.6: each component of T[v]−{v}\mathcal T[v]-\{v\}T[v]−{v} meets at most two enhancements of the clique;
  8. for a connected KtK_tKt​-minor-free graph, the adjacency matrix in any Lex-DFS order is g(t)g(t)g(t)-grid free;
  9. grid free implies mixed free for 0/10/10/1 matrices;
  10. the graph form of Theorem 5.8 with the explicit constant applied on p. 3:29.

The Helly theorem is a reference to an existing open platform statement rather than a duplicate draft. Bonnet et al., pp. 3:22–3:29.

What the bound supplies

The theorem supplies a uniform contraction-width bound for every fixed excluded clique minor. In particular, the paper applies it to planar graphs through exclusion of K5K_5K5​. Its numerical value is far from small; the article remarks that the planar specialization has billions of digits. Without the result, excluding a clique minor alone would not yield the particular twin-width certificate required for those applications. Bonnet et al., p. 3:29.

The mathematical result is proved in the cited paper. This mission asks for machine-checked statements and, eventually, proofs of its graph and matrix claims. The reusable output includes a precise partition model of graph twin-width, interval-division models of mixed and grid minors, and a graph statement that converts a mixed-free adjacency order into a contraction-width bound. No machine-checked proof of these new target items is claimed here.

The main obstruction

A dense pattern of nonzero matrix zones does not directly give a graph minor. A matrix zone merely contains an edge somewhere between two vertex blocks. Those blocks need not induce connected subgraphs, and contracting them as if they were connected would use operations unavailable to graph minors. The proof also needs a vertex order with strong enough structure to control how the relevant connected sets overlap. These are the points that separate the grid-free milestone from a direct application of the matrix grid theorem. Bonnet et al., pp. 3:26–3:29.

The conversion from a mixed-free adjacency matrix to graph twin-width has a second issue: matrix twin-width permits separate row and column contractions, while a graph contraction merges one vertex partition and must keep the two matrix axes synchronized. Theorem 5.8 addresses the graph case. The milestone uses the explicit constant that the paper applies at the end of Theorem 6.3, while the printed Theorem 5.8 states an asymptotic bound. Bonnet et al., pp. 3:22–3:23, 3:29.

Formalization scope

Graphs are finite simple graphs, and twin-width and the number of division parts are natural numbers. The real constants ckc_kck​ and f(t)f(t)f(t) are compared with natural-number twin-width through ⌊f(t)⌋\lfloor f(t)\rfloor⌊f(t)⌋; the explicit value is positive. Vertex orders are equivalences with Fin⁡(n)\operatorname{Fin}(n)Fin(n). Matrix divisions use strictly increasing cut points, so every part is a nonempty consecutive interval. The adjacency matrix has a zero diagonal because simple graphs have no loops. The graph-minor predicate and the subtree representation come from existing published definitions with the same conventions; graph twin-width and the matrix objects are local draft definitions shared across this mission. Bonnet et al., pp. 3:9, 3:12, 3:17–3:19.

The grid-free milestone holds for every Lex-DFS order, as in the paper, and the existence of such an order is a separate milestone. Lemmas 6.4–6.6 are stated in the proof's context: a DFS, and vertices ai,ja_{i,j}ai,j​, bi,jb_{i,j}bi,j​ and blocks BjB_jBj​ with exactly the order and adjacency properties the page uses. Lemma 6.5 is posed for every block except the last. As printed it also covers the last block Bg(t)/2B_{g(t)/2}Bg(t)/2​, and for that block it can fail. A five-vertex example satisfies every hypothesis of the lemma and violates the printed conclusion; it is checked in Lean in the sanity file, and the formal item records the change. The theorem's bound is uniform over all finite graphs on the excluded-minor hypothesis. A twin-width definition using an unrestricted real infimum, a sequence that skips directly to one part, red degree counted over homogeneous pairs, or a grid minor over arbitrary noninterval partitions would change the claim. Components and empty graphs use the endpoint conventions stated above. The k≥1k\ge1k≥1 hypothesis in the graph conversion milestone rules out the degenerate zero-division case.

Contributions toward the core grid-free order claim, the symmetric contraction bridge, the component theorem, and the reusable minor and division lemmas are within scope. The running-time claims elsewhere in the paper and the pointer construction and the two Kt,tK_{t,t}Kt,t​-minor constructions inside the proof of Theorem 6.3 are not separate milestones: they are unnumbered and their statements need the pointers. They remain part of the grid-free milestone.

Selected references

  • É. Bonnet, E. J. Kim, S. Thomassé, and R. Watrigant, Twin-width I: Tractable FO Model Checking, Journal of the ACM 69(1), Article 3, 2021. DOI:10.1145/3486655.
  • A. Marcus and G. Tardos, Excluded permutation matrices and the Stanley–Wilf conjecture, Journal of Combinatorial Theory, Series A 107(1), 153–160, 2004. DOI:10.1016/j.jcta.2004.04.002.
  • F. Gavril, The intersection graphs of subtrees in trees are exactly the chordal graphs, Journal of Combinatorial Theory, Series B 16(1), 47–56, 1974. DOI:10.1016/0095-8956(74)90094-X.
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Combinatorics·Captain: mikedeng1

Three-Coloring and List Three-Coloring of Graphs Without Induced Paths on Seven Vertices 3: In a Lemma 11 Instance, Two Vertices of Xᵢ Have Neighbor Triples Joined by an EdgeResearch Paper

Motivation

A graph is 3-colorable if its vertices can be colored with three colors so that adjacent vertices get different colors. Deciding 3-colorability is NP-complete in general, and one of the standard ways to map the boundary between easy and hard cases is to forbid an induced subgraph. For the path PtP_tPt​ on ttt vertices, 3-colorability of P6P_6P6​-free graphs was known to be polynomial (Randerath and Schiermeyer, 2004, reference [21] of the paper), and Randerath, Schiermeyer and Tewes asked in 2002 whether the same holds for t=7t = 7t=7.

Bonomo, Chudnovsky, Maceli, Schaudt, Stein and Zhong (Combinatorica, 2017) answered this question. Their Theorem 1 states that the list version, in which every vertex vvv may only receive a color from a prescribed list L(v)⊆{1,2,3}L(v) \subseteq \{1,2,3\}L(v)⊆{1,2,3}, can be decided for P7P_7P7​-free graphs in time O(∣V(G)∣21(∣V(G)∣+∣E(G)∣))O(|V(G)|^{21}(|V(G)|+|E(G)|))O(∣V(G)∣21(∣V(G)∣+∣E(G)∣)). The algorithm first reduces the instance to a polynomial number of configurations described by a seed, and then calls Lemma 11 (p. 14) on each configuration. Lemma 11 rests on one structural fact about P7P_7P7​-free graphs, Claim 13 (p. 16), which is the goal of this mission.

Timeline:

  • 2002: Randerath, Schiermeyer and Tewes ask whether 3-colorability of P7P_7P7​-free graphs is polynomial.
  • 2004: Randerath and Schiermeyer give a polynomial algorithm for P6P_6P6​-free graphs.
  • 2017: Bonomo et al. give a polynomial algorithm for list 3-coloring of P7P_7P7​-free graphs; Claims 12 and 13 are its structural core.
  • As of the paper, no ttt is known for which 3-coloring PtP_tPt​-free graphs is NP-complete (p. 2); the case t=8t = 8t=8 is open.

Setting

All graphs are finite and simple. For a set SSS of vertices of a graph GGG, N(S)N(S)N(S) is the set of vertices outside SSS with a neighbor in SSS, and S‾=S∪N(S)\overline{S} = S \cup N(S)S=S∪N(S). A graph is PtP_tPt​-free if it has no induced subgraph isomorphic to PtP_tPt​.

A palette LLL assigns each vertex a list L(v)⊆{1,2,3}L(v) \subseteq \{1,2,3\}L(v)⊆{1,2,3}. A seed of (G,L)(G,L)(G,L) is a nonempty set SSS that induces a connected subgraph, is 2-dominating (every vertex is at distance at most 222 from SSS), and satisfies ∣L(v)∣=1|L(v)| = 1∣L(v)∣=1 for v∈Sv \in Sv∈S and ∣L(v)∣=2|L(v)| = 2∣L(v)∣=2 for v∈N(S)v \in N(S)v∈N(S).

The Lemma 11 setting consists of a graph GGG, a palette LLL and a seed SSS such that

  1. GGG is connected and P7P_7P7​-free;
  2. adjacent v∈Sv \in Sv∈S and w∈N(S)w \in N(S)w∈N(S) have disjoint lists;
  3. the set XXX of vertices with ∣L(v)∣=3|L(v)| = 3∣L(v)∣=3 is stable and anticomplete to V(G)∖(S‾∪X)V(G) \setminus (\overline{S} \cup X)V(G)∖(S∪X);
  4. no vertex of XXX has a connected neighborhood.

For i∈{1,2,3}i \in \{1,2,3\}i∈{1,2,3}, DiD_iDi​ is the set of v∈N(S)v \in N(S)v∈N(S) with L(v)={1,2,3}∖{i}L(v) = \{1,2,3\} \setminus \{i\}L(v)={1,2,3}∖{i}, and Ni(x)=N(x)∩DiN_i(x) = N(x) \cap D_iNi​(x)=N(x)∩Di​. For {i,j,k}={1,2,3}\{i,j,k\} = \{1,2,3\}{i,j,k}={1,2,3}, XiX_iXi​ is the set of x∈Xx \in Xx∈X for which Nj(x)N_j(x)Nj​(x) is not complete to Nk(x)N_k(x)Nk​(x), and for x∈Xix \in X_ix∈Xi​ the paper fixes non-adjacent nj(x)∈Nj(x)n_j(x) \in N_j(x)nj​(x)∈Nj​(x), nk(x)∈Nk(x)n_k(x) \in N_k(x)nk​(x)∈Nk​(x).

Formalization targets

Goal: Claim 13 (p. 16)

In the Lemma 11 setting, let {i,j,k}={1,2,3}\{i,j,k\} = \{1,2,3\}{i,j,k}={1,2,3}, x,y∈Xix, y \in X_ix,y∈Xi​, and let nj∈Nj(x)n_j \in N_j(x)nj​∈Nj​(x), nk∈Nk(x)n_k \in N_k(x)nk​∈Nk​(x) be non-adjacent. Then

∃ a∈{x,nj,nk}, ∃ b∈{y,nj(y),nk(y)}:ab∈E(G).\exists\, a \in \{x, n_j, n_k\},\ \exists\, b \in \{y, n_j(y), n_k(y)\}:\quad ab \in E(G).∃a∈{x,nj​,nk​}, ∃b∈{y,nj​(y),nk​(y)}:ab∈E(G).

Milestones

  1. Display (1), p. 6. For a seed SSS and two non-adjacent v,w∈N(S)v, w \in N(S)v,w∈N(S) there is an induced vvv–www path on at least 333 vertices whose inner vertices lie in SSS.
  2. p. 15. For d∈Did \in D_id∈Di​ and s∈S∩N(d)s \in S \cap N(d)s∈S∩N(d), L(s)={i}L(s) = \{i\}L(s)={i}.
  3. p. 15. No vertex of XXX has a neighbor in SSS.
  4. Claim 12, p. 15. For i≠ji \ne ji=j, ui,vi∈Diu_i, v_i \in D_iui​,vi​∈Di​, uj,vj∈Dju_j, v_j \in D_juj​,vj​∈Dj​ with {ui,vi,uj,vj}\{u_i, v_i, u_j, v_j\}{ui​,vi​,uj​,vj​} stable, there is an induced path PPP with ends a,ba, ba,b among them such that {a,b}≠{ui,uj}\{a,b\} \ne \{u_i,u_j\}{a,b}={ui​,uj​}, {a,b}≠{vi,vj}\{a,b\} \ne \{v_i,v_j\}{a,b}={vi​,vj​}, the interior P∗P^*P∗ lies in SSS, and P∗P^*P∗ is anticomplete to the other two vertices.

Significance

Claim 13 says that the neighbor triples {x,nj(x),nk(x)}\{x, n_j(x), n_k(x)\}{x,nj​(x),nk​(x)} of the vertices of XiX_iXi​ pairwise touch. This is what allows the proof of Lemma 11 (Claims 14 to 17) to refine the palette of all of XiX_iXi​ simultaneously with only polynomially many guesses: without it, the vertices of XiX_iXi​ could interact independently and the number of cases would be exponential. Claim 12 and display (1) are the general tools for building long induced paths through a seed, used throughout §3.1.

The paper's result is proved and published. None of it is formalized in a proof assistant as far as the platform's index shows. This mission produces a machine-checked version of the structural core of Lemma 11, and the definitions it needs (seeds, the Lemma 11 setting, the sets DiD_iDi​ and Ni(x)N_i(x)Ni​(x)) are reusable for the rest of the paper's argument.

Difficulty

The proof of Claim 13 is short on paper, but it assembles an induced P7P_7P7​ from three pieces: the path through SSS given by Claim 12 and the two short paths nj−x−nkn_j - x - n_knj​−x−nk​ and nj(y)−y−nk(y)n_j(y) - y - n_k(y)nj​(y)−y−nk​(y). Showing that the union is induced requires every non-adjacency between the pieces, and these come from different hypotheses: the stability of XXX, the fact that XXX has no neighbor in SSS, the list structure of the vertices of SSS next to DiD_iDi​, and part (c) of Claim 12. Claim 12 itself is an extremal argument (choose the neighbor closest to an end of a path), which in a formal setting requires explicit manipulation of paths as sequences of vertices: splitting, reversing and concatenating them while keeping them induced. The obvious approach of taking any shortest path through SSS between two of the four vertices fails, because such a path can have interior vertices adjacent to the other two.

Formalization scope

Graphs are SimpleGraph V on a finite type V with decidable equality; vertex sets are Finset V. The colors 1,2,31,2,31,2,3 are 0,1,2 in Fin 3, and a palette is V → Finset (Fin 3). P7P_7P7​-freeness is the negation of Mathlib's induced containment IsIndContained of pathGraph 7; ordinary subgraph containment would be a different, much stronger condition. Connectedness uses Mathlib's Connected, which includes nonemptiness. An induced path is the published platform definition StrongPerfectGraph.Main.IsInducedPath (a nonempty list of distinct vertices in which two vertices are adjacent exactly when consecutive); its ends are the first and last list entries and its interior P∗P^*P∗ is the list with both ends removed.

The Lemma 11 setting is a structure Lemma11Hyp G L S bundling its hypotheses. Lemma 11's own conclusion, a decision procedure with running time O(∣V(G)∣9(∣V(G)∣+∣E(G)∣))O(|V(G)|^9(|V(G)|+|E(G)|))O(∣V(G)∣9(∣V(G)∣+∣E(G)∣)), and the running time of Theorem 1 are not formalized, since there is no machine model. The paper's arbitrary choice of nj(y),nk(y)n_j(y), n_k(y)nj​(y),nk​(y) is replaced by quantification over every non-adjacent pair mj∈Nj(y)m_j \in N_j(y)mj​∈Nj​(y), mk∈Nk(y)m_k \in N_k(y)mk​∈Nk​(y), which is Claim 13 for every possible choice. The case x=yx = yx=y is allowed.

One hypothesis is added to Claim 12: the four vertices ui,vi,uj,vju_i, v_i, u_j, v_jui​,vi​,uj​,vj​ are pairwise distinct, and the ends a,ba, ba,b of the path are required to be distinct. The paper's proof and its use in Claim 13 take the four vertices distinct; without the assumption the claim can fail, and without a≠ba \ne ba=b a one-vertex path would satisfy it vacuously.

The following trivializing readings are excluded: dropping the overline in "anticomplete to V(G)∖(S‾∪X)V(G) \setminus (\overline{S} \cup X)V(G)∖(S∪X)" (which, together with milestone 3, would force XXX to be isolated); a one-vertex path or repeated vertices in Claim 12; and subgraph instead of induced-subgraph containment for P7P_7P7​-freeness. The setting is satisfiable with X≠∅X \neq \emptysetX=∅: the 5-cycle y−a−s−t−b−yy - a - s - t - b - yy−a−s−t−b−y with S={s,t}S = \{s,t\}S={s,t}, L(s)={2}L(s) = \{2\}L(s)={2}, L(t)={3}L(t) = \{3\}L(t)={3}, L(a)={1,3}L(a) = \{1,3\}L(a)={1,3}, L(b)={1,2}L(b) = \{1,2\}L(b)={1,2}, L(y)={1,2,3}L(y) = \{1,2,3\}L(y)={1,2,3} satisfies every hypothesis, with y∈X1y \in X_1y∈X1​.

Proofs of the milestones and the goal are welcome, as are general lemmas about induced paths as lists (subpaths, concatenation through a connected set) that would serve other graph-theory missions.

Selected references

  • F. Bonomo, M. Chudnovsky, P. Maceli, O. Schaudt, M. Stein and M. Zhong, Three-coloring and list three-coloring of graphs without induced paths on seven vertices, Combinatorica 38 (2018) 779–801 (OnlineFirst 2017). https://doi.org/10.1007/s00493-017-3553-8
  • B. Randerath and I. Schiermeyer, 3-Colorability ∈ P for P₆-free graphs, Discrete Applied Mathematics 136 (2004) 299–313 (reference [21] of the paper).
  • B. Randerath, I. Schiermeyer and M. Tewes, Three-colorability and forbidden subgraphs. II: polynomial algorithms, Discrete Mathematics 251 (2002) 137–153 (reference [22] of the paper).
  • E. Camby and O. Schaudt, A new characterization of PkP_kPk​-free graphs, Algorithmica (reference [2] of the paper; the source of Theorem 4, which the other missions of this series use).
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CombinatoricsMathematical Logic·Captain: mikedeng1

Twin-width I: Tractable FO Model Checking 5: First-Order Interpretations of a Class of Graphs of Bounded Twin-width Have Bounded Twin-widthResearch Paper

Why interpretations

Twin-width is a graph parameter introduced by Bonnet, Kim, Thomassé and Watrigant in Twin-width I: Tractable FO Model Checking (J. ACM 69(1), Article 3, 2021; arXiv:2004.14789). Its main algorithmic consequence is that first-order model checking is fixed-parameter tractable on graphs given with a contraction sequence of bounded width. A parameter of this kind is useful only if the classes it bounds are closed under the constructions that arise in practice. Many such constructions are first-order definable: the complement of a graph, its square (join vertices at distance at most two), the map graph of a planar map. Section 8 of the paper shows that twin-width is stable under every such construction: a first-order interpretation of a class of bounded twin-width has bounded twin-width. Bounded VC-dimension, by contrast, is not preserved (interval graphs have VC-dimension at most two but interpret every graph, p. 3:41).

The result has since become the basis of the "model-theoretic" view of twin-width: bounded twin-width classes are closed under first-order transductions (Theorem 8.1 of the paper), and later work identifies them, among ordered structures, with the monadically dependent classes (Twin-width IV, Bonnet, Giocanti, Ossona de Mendez, Simon, Thomassé and Toruńczyk).

Setting

Let GGG be a finite simple graph on a vertex set VVV.

Two vertex sets X,YX,YX,Y are homogeneous if all pairs in X×YX\times YX×Y are edges or none is. For a partition P\mathcal PP of VVV, the red graph GPG_{\mathcal P}GP​ has the parts as vertices, two distinct parts being adjacent when they are not homogeneous. P\mathcal PP is a ddd-partition if GPG_{\mathcal P}GP​ has maximum degree at most ddd. The graph GGG has twin-width at most ddd, tww⁡(G)≤d\operatorname{tww}(G)\le dtww(G)≤d, if there is a sequence of ddd-partitions from the partition into singletons to a partition with at most one part, each obtained from the previous one by merging two parts (p. 3:32).

A prenex formula of depth ℓ\ellℓ with two free variables is

φ(x,y)=Q1x1 Q2x2⋯Qℓxℓ φ∗,Qi∈{∀,∃},\varphi(x,y)=Q_1x_1\,Q_2x_2\cdots Q_\ell x_\ell\ \varphi^*,\qquad Q_i\in\{\forall,\exists\},φ(x,y)=Q1​x1​Q2​x2​⋯Qℓ​xℓ​ φ∗,Qi​∈{∀,∃},

with φ∗\varphi^*φ∗ a Boolean combination of atoms u=vu=vu=v and E(u,v)E(u,v)E(u,v), u,v∈{x1,…,xℓ,x,y}u,v\in\{x_1,\dots,x_\ell,x,y\}u,v∈{x1​,…,xℓ​,x,y}. The interpretation φ(G)\varphi(G)φ(G) is the graph on VVV in which distinct u,vu,vu,v are adjacent iff G⊨φ(u,v)∧φ(v,u)G\models\varphi(u,v)\wedge\varphi(v,u)G⊨φ(u,v)∧φ(v,u). For a class G\mathcal GG, φ(G)\varphi(\mathcal G)φ(G) is the class of all induced subgraphs of the graphs φ(G)\varphi(G)φ(G), G∈GG\in\mathcal GG∈G.

The proof works with morphism-trees. The complete tree MTk(V)MT_k(V)MTk​(V) has as nodes all tuples (v1,…,vi)(v_1,\dots,v_i)(v1​,…,vi​) of vertices with i≤ki\le ki≤k, the parent of a tuple being its prefix. Two sibling nodes are equivalent in (G,P)(G,\mathcal P)(G,P) if an automorphism of the tree swaps them, preserving equalities and adjacencies among the entries of every tuple and the part of P\mathcal PP of every entry. A reduction deletes one of two equivalent siblings with its subtree, repeatedly; a reduct admits no further reduction. Two vertices u,u′u,u'u,u′ are adjacent in Eℓ+2(G,P)E_{\ell+2}(G,\mathcal P)Eℓ+2​(G,P) when (u)(u)(u) and (u′)(u')(u′) are equivalent in some reduction of MTℓ+2(V)MT_{\ell+2}(V)MTℓ+2​(V), and Iℓ+2(G,P)I_{\ell+2}(G,\mathcal P)Iℓ+2​(G,P) is the partition into the connected components of Eℓ+2(G,P)E_{\ell+2}(G,\mathcal P)Eℓ+2​(G,P).

Formalization targets

Goal: Theorem 8.3 (graphs)

For every prenex formula φ(x,y)\varphi(x,y)φ(x,y) and every ddd there is DDD such that

tww⁡(G)≤d ⟹ tww⁡(φ(G)[S])≤Dfor every graph G and every S⊆V(G).\operatorname{tww}(G)\le d\ \Longrightarrow\ \operatorname{tww}\bigl(\varphi(G)[S]\bigr)\le D\quad\text{for every graph } G \text{ and every } S\subseteq V(G).tww(G)≤d ⟹ tww(φ(G)[S])≤Dfor every graph G and every S⊆V(G).

The bound DDD depends on φ\varphiφ and ddd only. No explicit function is asserted: the paper gives none (the proof goes through a tower-type bound on reducts).

Milestones

  1. Lemma 5.2 in graph form (p. 3:18, applied on p. 3:43): rrr-refining hhh-partitions from the finest to the coarsest partition give tww⁡(G)≤r(h+1)\operatorname{tww}(G)\le r(h+1)tww(G)≤r(h+1).
  2. Lemma 7.3 (p. 3:32): reducts of MTℓ(G)MT_\ell(G)MTℓ​(G) have size bounded by a function of ℓ\ellℓ.
  3. Lemmas 7.11–7.14 (pp. 3:35–3:38): restriction to connected tuples rooted at a part and the pruned shuffle commute with reductions; the pruned shuffle of all MTℓ(G,P,X)MT_\ell(G,\mathcal P,X)MTℓ​(G,P,X) is MTℓ(G,P)MT_\ell(G,\mathcal P)MTℓ​(G,P), and that of reducts is a reduction of it.
  4. Lemma 8.4 (p. 3:41) and its extension to reductions (p. 3:42): equivalent nodes (u,v),(u,v′)(u,v),(u,v')(u,v),(u,v′) of MTℓ+2MT_{\ell+2}MTℓ+2​ satisfy the same prenex formulas of depth ℓ\ellℓ.
  5. Iℓ+2I_{\ell+2}Iℓ+2​ refines P\mathcal PP and is monotone under coarsening (p. 3:42).
  6. Lemma 8.5 (p. 3:42): a part of a ddd-partition meets boundedly many components of Eℓ+2(G,P)E_{\ell+2}(G,\mathcal P)Eℓ+2​(G,P).
  7. Lemma 8.6 (p. 3:43): parts of Iℓ+2(G,P)I_{\ell+2}(G,\mathcal P)Iℓ+2​(G,P) inside parts at distance at least 3ℓ+23^{\ell+2}3ℓ+2 in GPG_{\mathcal P}GP​ are homogeneous in φ(G)\varphi(G)φ(G).

Significance

Theorem 8.3 turns every first-order definable graph construction into a source of bounded-twin-width classes: squares and fixed powers of planar graphs, complements, map graphs (as transductions, via Theorem 8.1), kkk-planar graphs and bounded-degree string graphs. Combined with the model-checking algorithm it gives FPT first-order model checking on all these classes, provided a contraction sequence is available. The theorem is also the first step of the model-theoretic characterisations of bounded twin-width in the later papers of the series.

The result is proved in the paper; no machine-checked proof of it, or of any twin-width bound, exists to our knowledge, and Mathlib has no notion of twin-width or of first-order interpretations of graphs. The mission asks for a formal proof of the graph case. The morphism-tree milestones are also the combinatorial core of the paper's linear-time model-checking algorithm (Theorems 7.5, 7.15), whose running-time statements are not part of this mission.

Difficulty

The obvious approach refines the ddd-partitions Pi\mathcal P_iPi​ of GGG by the "type" of each vertex with respect to φ\varphiφ. The number of such types is not bounded: a vertex's behaviour depends on the whole graph through the quantifiers. The paper replaces types by the components of Eℓ+2(G,Pi)E_{\ell+2}(G,\mathcal P_i)Eℓ+2​(G,Pi​), defined through reductions of morphism-trees that respect Pi\mathcal P_iPi​. Two facts must then be shown, and neither is local in an obvious way. First, the number of components inside a part is bounded (Lemma 8.5); this needs the pruned shuffle, which assembles a reduction of the whole tree MTℓ+2(G,P)MT_{\ell+2}(G,\mathcal P)MTℓ+2​(G,P) from reductions of the local trees MTℓ+2(G,P,X)MT_{\ell+2}(G,\mathcal P,X)MTℓ+2​(G,P,X), and the fact that it commutes with reductions (Lemma 7.12), where an automorphism of one local tree must be extended to the shuffle without breaking adjacency between non-homogeneous parts. Second, far-apart components are homogeneous in φ(G)\varphi(G)φ(G) (Lemma 8.6), which needs the evaluation argument of Lemma 8.4 inside a reduction of the partitioned tree.

Formalization scope

  • Graphs are SimpleGraph V on a Fintype with decidable equality. Twin-width is the predicate TwinWidthLE G d in the paper's partition form (pp. 3:12, 3:32); trigraphs are not formalized, and twin-width is never an infimum, so no junk value of sInf can make a bound vacuous. Merge steps merge exactly two distinct parts, so a sequence cannot jump to one part.
  • Formulas are Mathlib's FirstOrder.Language.graph.Formula (Fin 2), evaluated in G.structure; variable 0 is xxx, variable 1 is yyy. Prenex is Mathlib's IsPrenex; quantifier depth qdepth is defined in the mission (∃=¬∀¬\exists=\neg\forall\neg∃=¬∀¬ counts once).
  • The interpretation requires u≠vu\neq vu=v for an edge (a simple graph has no loops). The goal is the graph case of Theorem 8.3, which the page states for augmented binary structures; Section 8 itself says it works with undirected graphs (p. 3:40).
  • In the goal the bound DDD is chosen after φ\varphiφ and ddd and before the graph: a bound chosen after the graph would make the statement trivial. The hereditary closure (induced subgraphs on every SSS) is part of the statement.
  • Morphism-trees are sets of tuples (Set (List V)), the paper's identification of a node with its current path (p. 3:30), which is exact for reductions of complete trees. Equivalent siblings are distinct. Distances in GPG_{\mathcal P}GP​ are in N∪{∞}\mathbb N\cup\{\infty\}N∪{∞}, so disconnected parts are far apart. The sequence graph uses 0-based indices, hence the exponent 3ℓ−(k+1)3^{\ell-(k+1)}3ℓ−(k+1). The pruned shuffle is defined by its characterisation (each component of the sequence graph of a tuple lies in the tree of its local root). Lemmas of §7.3 carry its standing assumption ℓ>0\ell>0ℓ>0.
  • Lemma 5.2 is posed for graphs with the bound r(h+1)r(h+1)r(h+1) (the matrix lemma's rtrtrt with t=h+1t=h+1t=h+1).
  • Not in the mission: Theorem 8.1 (transductions), Lemma 8.2 and Lemma 5.1 (augmented binary structures), and all running-time statements (Theorems 1.1, 7.1, 7.5, 7.15, Corollaries 7.16–7.17).

Reusable beyond this mission: the partition form of twin-width, quantifier depth of first-order formulas, and the morphism-tree machinery (reductions, pruned shuffles), which formalizes the algorithmic core of first-order model checking on bounded twin-width. Contributions welcome: proofs of the §7 lemmas, of Lemma 8.4 by induction on the quantifier prefix, and the closure of twin-width under induced subgraphs.

Selected references

  • É. Bonnet, E. J. Kim, S. Thomassé, R. Watrigant, Twin-width I: Tractable FO Model Checking, J. ACM 69(1), Article 3, 2021. https://doi.org/10.1145/3486655 (arXiv:2004.14789, https://arxiv.org/abs/2004.14789)
  • É. Bonnet, U. Giocanti, P. Ossona de Mendez, P. Simon, S. Thomassé, S. Toruńczyk, Twin-width IV: Ordered Graphs and Matrices, J. ACM, 2024. https://arxiv.org/abs/2102.03117
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Operations ResearchProbabilityStochastic Systems·Captain: mikedeng1

Graphon Mean Field Systems V: On Percolated Graphs Sampled From a Lipschitz Graphon the Mean-Square Error Is at Most κ(q)/(nβₙ)^{1/q}Research Paper

Motivation

Large systems of interacting diffusions — neurons, oscillators, agents in a network game, particles in a queueing or load-balancing model — are usually analysed through their mean-field limit: as the population grows, each particle interacts with the empirical law of the others, and the system is replaced by a single nonlinear (McKean–Vlasov) equation. The classical theory assumes that every particle interacts with every other one with the same strength. Real networks are heterogeneous and sparse: particle iii interacts with particle jjj only through an edge of a random graph, and the number of neighbours of a particle is much smaller than the population size.

Bayraktar, Chakraborty and Wu (Ann. Appl. Probab. 33(5), 2023) describe the heterogeneity by a graphon and prove laws of large numbers and rates of convergence for such systems. This mission is the fifth of a series formalizing that paper. Its subject is the paper's quantitative result for not-so-dense graphs: when the edges are sampled from a Lipschitz graphon and then thinned by a sparsity factor βn\beta_nβn​, the average mean-square distance between each particle and its graphon limit is of order (nβn)−1/q(n\beta_n)^{-1/q}(nβn​)−1/q for every q>1q>1q>1.

Setting

Let I=[0,1]I=[0,1]I=[0,1] with Lebesgue measure. A graphon is a measurable symmetric function G:I×I→[0,1]G:I\times I\to[0,1]G:I×I→[0,1]. Fix a horizon T>0T>0T>0 and write Cd=C([0,T]:Rd)\mathcal C_d=C([0,T]:\mathbb R^d)Cd​=C([0,T]:Rd) with the norm ∥x∥∗,T=sup⁡0≤s≤T∣xs∣\|x\|_{*,T}=\sup_{0\le s\le T}|x_s|∥x∥∗,T​=sup0≤s≤T​∣xs​∣.

One probability space carries, for every label u∈Iu\in Iu∈I, an initial state Xu(0)X_u(0)Xu​(0) with law μu(0)\mu_u(0)μu​(0) and a standard ddd-dimensional Brownian motion BuB_uBu​, the whole family {Xu(0),Bu:u∈I}\{X_u(0),B_u:u\in I\}{Xu​(0),Bu​:u∈I} being independent. The limit system (4.2) is a continuum of diffusions, one per label:

Xu(t)=Xu(0)+∫0t ⁣∫I ⁣∫Rdb(Xu(s),x) G(u,v) μv,s(dx) dv ds+∫0tσ(Xu(s)) dBu(s),μu,t=L(Xu(t)).X_u(t)=X_u(0)+\int_0^t\!\int_I\!\int_{\mathbb R^d}b(X_u(s),x)\,G(u,v)\,\mu_{v,s}(dx)\,dv\,ds+\int_0^t\sigma(X_u(s))\,dB_u(s),\qquad \mu_{u,t}=\mathcal L(X_u(t)).Xu​(t)=Xu​(0)+∫0t​∫I​∫Rd​b(Xu​(s),x)G(u,v)μv,s​(dx)dvds+∫0t​σ(Xu​(s))dBu​(s),μu,t​=L(Xu​(t)).

The particles are independent but not identically distributed; their laws are coupled through the dvdvdv-integral.

The not-so-dense nnn-particle system (4.1) uses the same initial states and Brownian motions at the labels i/ni/ni/n:

Xin(t)=Xi/n(0)+∫0t1nβn∑j=1nξijn b(Xin(s),Xjn(s)) ds+∫0tσ(Xin(s)) dBi/n(s),i=1,…,n.X^n_i(t)=X_{i/n}(0)+\int_0^t\frac{1}{n\beta_n}\sum_{j=1}^n\xi^n_{ij}\,b(X^n_i(s),X^n_j(s))\,ds+\int_0^t\sigma(X^n_i(s))\,dB_{i/n}(s),\qquad i=1,\dots,n.Xin​(t)=Xi/n​(0)+∫0t​nβn​1​j=1∑n​ξijn​b(Xin​(s),Xjn​(s))ds+∫0t​σ(Xin​(s))dBi/n​(s),i=1,…,n.

The edges are percolated samples of GGG (Condition 4.3): ξijn=ξjin∼Bernoulli(βnG(i/n,j/n))\xi^n_{ij}=\xi^n_{ji}\sim\mathrm{Bernoulli}(\beta_nG(i/n,j/n))ξijn​=ξjin​∼Bernoulli(βn​G(i/n,j/n)), independent for 1≤i≤j≤n1\le i\le j\le n1≤i≤j≤n and independent of the noise. A particle has about nβnn\beta_nnβn​ neighbours, which is why the interaction is scaled by 1/(nβn)1/(n\beta_n)1/(nβn​).

The standing hypotheses are Condition 4.1 — u↦μu(0)u\mapsto\mu_u(0)u↦μu​(0) measurable with bounded second moments; bbb bounded and Lipschitz; σ\sigmaσ bounded, Lipschitz and invertible with bounded inverse; βn∈(0,1]\beta_n\in(0,1]βn​∈(0,1] and nβn→∞n\beta_n\to\inftynβn​→∞ — and Condition 2.3: on finitely many intervals I1,…,INI_1,\dots,I_NI1​,…,IN​ covering III, the map u↦μu(0)u\mapsto\mu_u(0)u↦μu​(0) is Lipschitz in the Wasserstein distance W2W_2W2​, and GGG is Lipschitz on every block Ii×IjI_i\times I_jIi​×Ij​.

Formalization targets

Goal: Theorem 4.2

Under Conditions 2.3, 4.1 and 4.3, for each q∈(1,∞)q\in(1,\infty)q∈(1,∞) there is κ(q)∈(0,∞)\kappa(q)\in(0,\infty)κ(q)∈(0,∞) with

1n∑i=1nE∥Xin−Xi/n∥∗,T2≤κ(q)(nβn)1/qfor all n∈N.\frac1n\sum_{i=1}^n\mathbb E\big\|X^n_i-X_{i/n}\big\|_{*,T}^2\le\frac{\kappa(q)}{(n\beta_n)^{1/q}}\qquad\text{for all }n\in\mathbb N .n1​i=1∑n​E​Xin​−Xi/n​​∗,T2​≤(nβn​)1/qκ(q)​for all n∈N.

The constant depends on qqq and on the data, never on nnn. The bound is on the average over particles, not on the maximum.

Milestones

  1. Lemma 7.1: all moments of the coupling error are bounded uniformly in nnn and iii.
  2. (7.10): E[ξijn∣Xjn(s)−Xj/n(s)∣2]≤(2E∣Xjn(s)−Xj/n(s)∣2+κ(q)(nβn)−1/q)βnG(i/n,j/n)\mathbb E[\xi^n_{ij}|X^n_j(s)-X_{j/n}(s)|^2]\le\big(2\mathbb E|X^n_j(s)-X_{j/n}(s)|^2+\kappa(q)(n\beta_n)^{-1/q}\big)\beta_nG(i/n,j/n)E[ξijn​∣Xjn​(s)−Xj/n​(s)∣2]≤(2E∣Xjn​(s)−Xj/n​(s)∣2+κ(q)(nβn​)−1/q)βn​G(i/n,j/n).
  3. (7.15): the interaction error Rsn,2R^{n,2}_sRsn,2​ is at most (κ/n)∑jE∣Xjn(s)−Xj/n(s)∣2+κ(q)(nβn)−1/q(\kappa/n)\sum_j\mathbb E|X^n_j(s)-X_{j/n}(s)|^2+\kappa(q)(n\beta_n)^{-1/q}(κ/n)∑j​E∣Xjn​(s)−Xj/n​(s)∣2+κ(q)(nβn​)−1/q.
  4. Theorem 2.1(b) for (4.2): W2,T(μu,μv)≤κ∣u−v∣W_{2,T}(\mu_u,\mu_v)\le\kappa|u-v|W2,T​(μu​,μv​)≤κ∣u−v∣ for u,vu,vu,v in one interval IiI_iIi​.
  5. §7.3 display: the discretization error Rsn,4R^{n,4}_sRsn,4​ is at most κ/n2\kappa/n^2κ/n2.

Significance

Theorem 4.2 gives an explicit rate for the convergence of a particle system on a sparse random graph to its graphon limit. Its companion, Theorem 4.1 (mission IV), proves the law of large numbers under weaker hypotheses but without a rate. The rate says that the effective sample size is nβnn\beta_nnβn​, the typical degree, not nnn: graphs with βn→0\beta_n\to0βn​→0 still approximate the graphon system as long as the degree grows. Results of this type justify replacing a large heterogeneous network by its graphon limit in control and game problems, such as graphon mean-field games, where the error of the replacement must be quantified.

The paper proves the theorem by hand; none of its results has a machine-checked proof. The formalization produces precise statements of the not-so-dense model, of the estimates of §7.1 that hold for any sparsity, and of the discretization bound, each usable on its own. The milestones (7.10) and (7.15) are the paper's key estimates for sparse interactions and are shared with mission IV.

Difficulty

The obvious approach is to compare each XinX^n_iXin​ with Xi/nX_{i/n}Xi/n​ by Gronwall's inequality, after bounding the difference of drifts. That step fails at the interaction term: the edge ξijn\xi^n_{ij}ξijn​ and the error Xjn−Xj/nX^n_j-X_{j/n}Xjn​−Xj/n​ are dependent, because the edge enters the dynamics of particle jjj. A plain Cauchy–Schwarz bound loses a factor 1/βn1/\beta_n1/βn​ and gives nothing when βn→0\beta_n\to0βn​→0. Estimates (7.10) and (7.15) quantify how weak this dependence is, and they are where the hypotheses that σ\sigmaσ is invertible with bounded inverse and that nβn→∞n\beta_n\to\inftynβn​→∞ are needed. The discretization term needs the Lipschitz regularity of the limit laws in the label (Theorem 2.1(b)), itself a fixed-point estimate on a space of measure-valued maps.

Formalization scope

The Lean development uses Mathlib's unitInterval for III, Fin d → ℝ with its sup norm for Rd\mathbb R^dRd (all constants are existential, so the choice of norm does not matter), time ℝ≥0, and the path space Cd\mathcal C_dCd​ as continuous maps on [0,T][0,T][0,T] with the sup norm. Brownian motions, Itô integrals and Itô processes are those of the published Peng1990.SMP.Stochastic; Wasserstein distances are the published WassersteinDRO.Duality.wassersteinDistance, valued in [0,∞][0,\infty][0,∞]. Particle iii is Lean's i : Fin n with label (i+1)/n(i+1)/n(i+1)/n.

Committed conventions:

  • A solution of (4.2) has continuous paths, AE-measurable path maps, and path laws in the class M\mathcal MM (measurable in uuu, bounded second moments); each XuX_uXu​ is a strong solution for the filtration of (Xu(0),Bu)(X_u(0),B_u)(Xu​(0),Bu​). A solution of (4.1) is a strong solution for the filtration of the edges, the initial states and the Brownian motions of the nnn labels.
  • The edges are {0,1}\{0,1\}{0,1}-valued, symmetric, with the diagonal included, mutually independent over i≤ji\le ji≤j, and independent of the σ-algebra of the noise.
  • Condition 4.1(d) is required for n≥1n\ge1n≥1; the invertibility of σ\sigmaσ is that of a d×dd\times dd×d matrix; the intervals of Condition 2.3 are order-connected subsets of III.
  • Expectations of nonnegative quantities are lower Lebesgue integrals, so no expectation is silently 000 for a non-integrable integrand.

The solution predicates are not vacuous: the zero-coefficient system Xu(t)=Xu(0)X_u(t)=X_u(0)Xu​(t)=Xu​(0) satisfies both, Condition 4.1 and Condition 4.3 are satisfiable, and every statement quantifies over solutions rather than assuming the conclusion. The constant κ(q)\kappa(q)κ(q) is chosen after qqq and before nnn, so it cannot depend on nnn, and the bound is the paper's average over iii, not a weaker or a stronger form. At n=0n=0n=0 both sides of every rate are 000.

The paper's proofs are in its §5 (Theorem 2.1), §7.1 (Lemma 7.1, (7.10), (7.15)) and §7.3 (Theorem 4.2). Stochastic analysis at that level — Burkholder–Davis–Gundy and Rosenthal inequalities, Girsanov's theorem for these SDEs, Gronwall arguments for measure-valued fixed points — is mostly absent from Mathlib, and contributions of such infrastructure are welcome, as are proofs of any milestone.

Selected references

  • E. Bayraktar, S. Chakraborty, R. Wu, Graphon mean field systems, Ann. Appl. Probab. 33(5):3587–3619, 2023. https://doi.org/10.1214/22-AAP1901
  • L. Lovász, Large Networks and Graph Limits, AMS Colloquium Publications 60, 2012. https://doi.org/10.1090/coll/060
  • B. Bollobás, C. Borgs, J. Chayes, O. Riordan, Percolation on dense graph sequences, Ann. Probab. 38(1):150–183, 2010. https://doi.org/10.1214/09-AOP478
  • S. Peng, A general stochastic maximum principle for optimal control problems, SIAM J. Control Optim. 28(4):966–979, 1990. https://doi.org/10.1137/0328054
11 thms1 active userReviewed
Operations ResearchProbabilityStochastic Systems·Captain: mikedeng1

Graphon Mean Field Systems IV: Law of Large Numbers for Particle Systems on Percolated Not-So-Dense Graphs With nβₙ → ∞Research Paper

Motivation

Large networks are often modeled through a graphon, a measurable function that records how likely two labeled vertices are to interact. For interacting diffusions, this lets one ask whether a finite random network has a predictable population limit. Bayraktar, Chakraborty and Wu study that question for graphon weighted systems, including graphs obtained by independently retaining potential edges with a sparsity parameter βn\beta_nβn​. Their not-so-dense regime allows βn\beta_nβn​ to decrease, provided the typical interaction scale nβnn\beta_nnβn​ diverges. Theorem 4.1 is the paper's law of large numbers for this regime; its two conclusions distinguish convergence of the population's empirical law from mean-square closeness of each particle to a coupled graphon limit. Bayraktar, Chakraborty and Wu, 2023.

The authors present three main convergence results: continuity of the continuum graphon system in Section 2, a dense-system law of large numbers in Section 3, and the percolated-system law of large numbers in Section 4. Section 7 establishes the latter. The graphon and cut-metric framework used here follows the graph-limit treatment cited by the paper. Bayraktar, Chakraborty and Wu, 2023; Lovász, 2012.

Setting

Let I=[0,1]I=[0,1]I=[0,1]. A graphon is a symmetric measurable G:I2→[0,1]G:I^2\to[0,1]G:I2→[0,1]. The cut norm ∥W∥□\|W\|_\square∥W∥□​ measures the largest absolute integral of WWW over a measurable rectangle S×US\times US×U. A sequence of step graphons GnG_nGn​ approaches GGG when ∥Gn−G∥□→0\|G_n-G\|_\square\to0∥Gn​−G∥□​→0. The related operator norm ∥W∥∞→1\|W\|_{\infty\to1}∥W∥∞→1​ measures the effect of WWW on bounded measurable functions. Remark 2.1 says cut convergence implies convergence in this operator norm. Bayraktar, Chakraborty and Wu, 2023, Remark 2.1.

Fix a dimension d≥1d\ge1d≥1 and horizon T>0T>0T>0. A label u∈Iu\in Iu∈I has an initial state Xu(0)∈RdX_u(0)\in\mathbb R^dXu​(0)∈Rd and an independent ddd-dimensional Brownian motion BuB_uBu​. The initial states are independent of each other and of the Brownian motions. The limiting graphon particle XuX_uXu​ is a continuous path whose drift averages b(Xu(s),x)G(u,v)b(X_u(s),x)G(u,v)b(Xu​(s),x)G(u,v) against the time-sss laws of all labels vvv, and whose diffusion matrix is σ(Xu(s))\sigma(X_u(s))σ(Xu​(s)). The time-sss law at label vvv is μv,s=L(Xv(s))\mu_{v,s}=\mathcal L(X_v(s))μv,s​=L(Xv​(s)). The family of path laws μu=L(Xu)\mu_u=\mathcal L(X_u)μu​=L(Xu​) is measurable in uuu and has uniformly bounded second moments. Bayraktar, Chakraborty and Wu, 2023, (4.2).

For nnn particles, label iii means i/ni/ni/n, with 1≤i≤n1\le i\le n1≤i≤n. Each undirected edge weight ξijn=ξjin\xi^n_{ij}=\xi^n_{ji}ξijn​=ξjin​ is Bernoulli with mean βnGn(i/n,j/n)\beta_nG_n(i/n,j/n)βn​Gn​(i/n,j/n); diagonal weights are included. These edge variables are independent of the Brownian motions and initial states. The finite particle XinX_i^nXin​ uses the same initial state and Brownian motion as Xi/nX_{i/n}Xi/n​. Its drift is the sum of ξijnb(Xin,Xjn)\xi^n_{ij}b(X_i^n,X_j^n)ξijn​b(Xin​,Xjn​) divided by nβnn\beta_nnβn​; its diffusion is σ(Xin)\sigma(X_i^n)σ(Xin​). The coefficients bbb and σ\sigmaσ are bounded and Lipschitz, and σ(x)\sigma(x)σ(x) is invertible with uniformly bounded inverse. Initial laws have uniformly bounded second moments, 0<βn≤10<\beta_n\le10<βn​≤1, and nβn→∞n\beta_n\to\inftynβn​→∞. Bayraktar, Chakraborty and Wu, 2023, Conditions 4.1–4.2.

Formalization targets

Let Cd=C([0,T];Rd)\mathcal C_d=C([0,T];\mathbb R^d)Cd​=C([0,T];Rd), μn=n−1∑i=1nδXin\mu^n=n^{-1}\sum_{i=1}^{n}\delta_{X_i^n}μn=n−1∑i=1n​δXin​​, and μˉ=∫Iμu du\bar\mu=\int_I\mu_u\,duμˉ​=∫I​μu​du. The main target, Theorem 4.1, first asserts convergence in probability in the weak topology on P(Cd)\mathcal P(\mathcal C_d)P(Cd​):

μn→Pμˉ.\mu^n\xrightarrow{\mathbb P}\bar\mu.μnP​μˉ​.

This uses Condition 2.2(a): on each of finitely many intervals covering III, the initial law varies continuously in Wasserstein distance W2W_2W2​. If the graphon also satisfies Condition 2.2(b), its stated sectional continuity outside null sets, the theorem adds

1n∑i=1nE∥Xin−Xi/n∥∗,T2⟶0,∥x∥∗,T=sup⁡0≤s≤T∣xs∣.\frac1n\sum_{i=1}^{n}\mathbb E\|X_i^n-X_{i/n}\|_{*,T}^2\longrightarrow0, \qquad \|x\|_{*,T}=\sup_{0\le s\le T}|x_s|.n1​i=1∑n​E∥Xin​−Xi/n​∥∗,T2​⟶0,∥x∥∗,T​=0≤s≤Tsup​∣xs​∣.

The milestones record the paper's cut-to-operator observation, uniform coupling moments, the one-edge and two-edge estimates, the bound on the second interaction-error term, the quantitative coupling lemma, and the empirical law of large numbers for the independent limiting particles. The second displayed limit is conditional on 2.2(b); the first is not. Bayraktar, Chakraborty and Wu, 2023, Theorem 4.1.

Significance

The first conclusion identifies the population-level limit even when the sampled network has a decreasing edge-retention probability. It gives a deterministic law on path space, rather than only a limit at one time. The second conclusion is stronger: it compares each finite particle with the limiting particle built from the same initial state and Brownian motion, averaged over all labels. This comparison is available when the extra graphon continuity assumption holds. Bayraktar, Chakraborty and Wu, 2023, Theorem 4.1 and Remark 4.1.

The mathematical results were proved in the published paper. The present mission asks for machine-checked Lean proofs of its specified statements; the draft theorems themselves currently contain sorry. Reusable outputs would include the percolated-edge model, a path-law formulation of graphon diffusions, and estimates for random interactions that depend on the particle states they influence. The paper's proofs appear in Sections 5–7, with the sparse-system argument in Section 7. Bayraktar, Chakraborty and Wu, 2023.

Difficulty

The edge variables are independent of the driving noise, but a particle's state depends on its incident edges. Therefore an expression such as E[ξijn∣Xjn−Xj/n∣2]\mathbb E[\xi^n_{ij}|X_j^n-X_{j/n}|^2]E[ξijn​∣Xjn​−Xj/n​∣2] cannot be factored into the edge mean times the error moment. The same dependence affects pairs of edges in the squared interaction sum. Equations (7.10), (7.14), and (7.15) quantify the discrepancy. A second difficulty is that cut convergence controls integrals against fixed bounded tests, while the dynamics and their path laws supply tests that vary with the system. The paper's regularity conditions and quantitative estimates are needed to connect those forms of control. Bayraktar, Chakraborty and Wu, 2023, Sections 7.1–7.2.

Formalization scope

Lean represents III by Mathlib's unit interval, Rd\mathbb R^dRd by Fin d → ℝ, and continuous paths by maps from [0,T][0,T][0,T] into that vector space. The chosen vector and matrix norms are fixed sup-type norms; finite-dimensional norm equivalence preserves the limit statements and the estimates with existential constants. The horizon and dimension are explicitly positive. Particle index i : Fin n denotes the paper's (i+1)(i+1)(i+1)st particle, and empirical laws use positive sizes n+1n+1n+1 to avoid division by zero. Wasserstein distances use extended nonnegative reals, while nonnegative expectations use lower integrals.

The noise lives on one probability space. The continuum equation uses the natural filtration of its own initial state and Brownian motion. The finite equation uses the natural filtration of its edge array, sampled initial states, and Brownian motions. A solution carries a measurable continuous-path law family in the paper's class M\mathcal MM; this prevents a nonmeasurable pushforward or a default-zero integral from silently satisfying an equation. Edge symmetry, Bernoulli marginals, independence, and diagonal self-loops are explicit. The zero-coefficient system has a separate sorry-free Lean witness for both solution predicates, ruling out a vacuous solution concept.

The formalization uses the published Peng1990.SMP.Stochastic Itô interface and WassersteinDRO.Duality.wassersteinDistance definition. Contributions can prove the listed estimates, strengthen their reusable stochastic and measure-theoretic infrastructure, or connect this mission's local setting to the other graphon missions after their definitions are published. The auxiliary changed-measure particles of Section 7.1 are outside the initial milestone list. Bayraktar, Chakraborty and Wu, 2023, Sections 4 and 7.

Selected references

  • E. Bayraktar, S. Chakraborty and R. Wu, Graphon mean field systems, Annals of Applied Probability 33(5):3587–3619, 2023. DOI.
  • L. Lovász, Large Networks and Graph Limits, American Mathematical Society Colloquium Publications 60, 2012. DOI.
13 thms1 active userReviewed
CombinatoricsTheoretical Computer Science·Captain: mikedeng1

Twin-width I: Tractable FO Model Checking 3: Grid Minor Theorem for Twin-width — t-Twin-Ordered Matrices Are (2t+2)-Mixed Free, and t-Mixed Free Matrices Have Twin-width at Most 4c_t·α^(4c_t+2)Research Paper

Motivation

Twin-width is a graph and matrix invariant introduced by Bonnet, Kim, Thomassé and Watrigant (J. ACM 69(1), Article 3, 2021). Classes of bounded twin-width include bounded tree-width and clique-width graphs, proper minor-closed classes, posets of bounded width, and permutations avoiding a fixed pattern; on all of them, first-order model checking is fixed-parameter tractable once a contraction sequence is given. The difficulty in applying this framework is to bound twin-width for a concrete class, because a contraction sequence is an intricate object.

Section 5 of the paper supplies the tool that makes such bounds routine: the Grid Minor Theorem for twin-width (Theorem 5.4). It reduces bounding twin-width to finding an order of the rows and columns of a matrix (for a graph, an order of its vertices) in which no large mixed minor appears. The paper then uses it for pattern-avoiding permutations, posets of bounded width and KtK_tKt​-minor free graphs (Section 6, Table 1). The argument is modelled on the proof of the Stanley–Wilf conjecture by Marcus and Tardos (JCTA 107, 2004) and on its use by Guillemot and Marx for permutation pattern matching (SODA 2014).

Timeline. Füredi and Hajnal (1992) conjectured a linear bound on the number of 111s in a 0,10,10,1-matrix avoiding a permutation pattern. Marcus and Tardos (2004) proved it, with constant ct=2t4(t2t)c_t=2t^4\binom{t^2}{t}ct​=2t4(tt2​), via ttt-grid minors. Fox (arXiv:1310.8378) improved the constant to 3t 28t3t\,2^{8t}3t28t, and Cibulka and Kynčl (arXiv:1607.07491) to 83(t+1)224t\tfrac83(t+1)^22^{4t}38​(t+1)224t. Guillemot and Marx (2014) built from the grid structure a decomposition of permutations; Bonnet et al. (2020 preprint, 2021 journal) generalized it to arbitrary matrices over a finite alphabet, which is Theorem 5.4.

Setting

Let M=(mi,j)M=(m_{i,j})M=(mi,j​) be an n×mn\times mn×m matrix with entries in a finite alphabet AAA of size α\alphaα. For sets RRR of rows and CCC of columns, the zone R∩CR\cap CR∩C is the submatrix on those rows and columns; it is constant if all its entries are equal.

A partition of MMM is a pair (R,C)(\mathcal R,\mathcal C)(R,C) of a partition of the rows and a partition of the columns. Its error value is the largest number, over all parts RiR_iRi​ (respectively CjC_jCj​), of parts of the other side forming a non-constant zone with it. A contraction sequence is a sequence of partitions starting from the finest one (all singletons), ending at the coarsest one (one row part, one column part), each obtained from the previous by merging two row parts or two column parts. MMM has twin-width at most ttt, tww⁡(M)≤t\operatorname{tww}(M)\le ttww(M)≤t, if some contraction sequence has error value at most ttt throughout.

A division is a partition whose parts consist of consecutive indices. MMM is ttt-twin-ordered if it has a contraction sequence of error value at most ttt consisting of divisions.

A zone is vertical if its rows are all equal, horizontal if its columns are all equal, and mixed otherwise. A ttt-mixed minor is a division into ttt row intervals and ttt column intervals all of whose t2t^2t2 zones are mixed; MMM is ttt-mixed free if it has none. For 0,10,10,1-matrices, a ttt-grid minor is such a division in which every zone contains a 111. Finally ct=83(t+1)224tc_t=\tfrac83(t+1)^22^{4t}ct​=38​(t+1)224t.

Formalization targets

Goal: Theorem 5.4

For n,m≥1n,m\ge1n,m≥1:

$$M\ \text{ttt-twin-ordered}\ \Longrightarrow\ M\ \text{(2t+2)(2t+2)(2t+2)-mixed free},

t\ge1,\ M\ \text{ttt-mixed free}\ \Longrightarrow\ \operatorname{tww}(M)\le 4c_t,\alpha^{4c_t+2}.$$

The constant is the one the paper's proof produces; the paper's summary 22O(t)2^{2^{O(t)}}22O(t) is not formalized.

Milestones, in the order of the proof

  1. Lemma 5.5: a matrix is mixed iff it contains a corner, a mixed 2×22\times22×2 submatrix on consecutive rows and columns.
  2. Lemma 5.6: fusing two consecutive row parts does not increase the mixed value of a column interval (mixed zones plus mixed cuts).
  3. Lemma 5.2: a sequence of partitions, each rrr-refining the next, with error value at most ttt, certifies tww⁡(M)≤rt\operatorname{tww}(M)\le rttww(M)≤rt.
  4. Theorem 5.3 (Marcus–Tardos, constant of Cibulka–Kynčl): ctmax⁡(n,m)c_t\max(n,m)ct​max(n,m) entries 111 force a ttt-grid minor.
  5. Lemma 5.7: a ttt-mixed free matrix has a division sequence of mixed value at most 2ct2c_t2ct​.
  6. The first item of Theorem 5.4.
  7. The row-type count on p. 3:22: within a row part, rows restricted to the non-mixed zones take at most αt′+1\alpha^{t'+1}αt′+1 values.

Significance

Theorem 5.4 characterizes bounded twin-width of matrices, up to the order of rows and columns, by the exclusion of a mixed minor. Its second item is used in Section 6 of the same paper to show that KtK_tKt​-minor free graphs, posets of bounded width and pattern-avoiding permutations have bounded twin-width, and in later papers of the twin-width series as the standard route to twin-width upper bounds. Without it, each such bound needs its own explicit contraction sequence.

The result is proved in the paper; no machine-checked proof of it exists in Mathlib or on the platform. The mission produces a machine-checked statement and, once solved, a proof of the grid theorem with its explicit constant, a formal treatment of contraction sequences of matrices, and a statement of the Marcus–Tardos theorem with the Cibulka–Kynčl constant, which is itself absent from Mathlib.

Difficulty

The first item is comparatively short. The second item is where the work lies. The natural first idea — contract greedily as long as the error value stays below a bound depending on ttt — does not work: the error value is not monotone under fusions, and nothing in the absence of a mixed minor directly prevents a greedy sequence from getting stuck with large error. Any proof has to identify a quantity that is monotone under fusion and is bounded by a density argument, and then turn a sequence controlled by that quantity into a contraction sequence of bounded error value; the paper's choices (mixed value, Lemmas 5.6 and 5.7) are the milestones. The density argument rests on the Marcus–Tardos theorem with an explicit single-exponential constant, a substantial combinatorial result in its own right and not available in Mathlib.

Formalization scope

Matrices are Matrix (Fin n) (Fin m) A with [Fintype A], and α\alphaα is Fintype.card A. Partitions are Mathlib Finpartitions of univ; the finest is ⊥, and "coarsest" means at most one part. Twin-width is the partition form of p. 3:18, as a predicate MatTwinWidthLE M t with exactly one merge, of rows or of columns, per step; it is never an infimum over a possibly empty set. Divisions in minors are given by strictly increasing cut points 0=r0<⋯<rt=n0=r_0<\dots<r_t=n0=r0​<⋯<rt​=n, so all parts are non-empty. Vertical and horizontal mean "all rows (columns) of the zone agree". Grid minors use Bool entries. Since ctc_tct​ is not an integer, α4ct+2\alpha^{4c_t+2}α4ct​+2 is a real power, and the bound is ⌊4ctα4ct+2⌋\lfloor 4c_t\alpha^{4c_t+2}\rfloor⌊4ct​α4ct​+2⌋; the mixed-value bound of Lemma 5.7 is ⌊2ct⌋\lfloor 2c_t\rfloor⌊2ct​⌋.

Hypotheses added relative to the page: n,m≥1n,m\ge1n,m≥1 in Theorems 5.3, 5.4 and Lemma 5.7; t≥1t\ge1t≥1 in the second item of Theorem 5.4, in Theorem 5.3 and in Lemma 5.7, since for t=0t=0t=0 every non-empty matrix is vacuously 000-mixed free and the bound would be false; m≥1m\ge1m≥1 in the row-type count. Theorem 5.3 is stated with the explicit constant ctc_tct​ that the paper quotes and uses, not as a bare existence statement.

The statements are not trivializable by the usual routes: twin-width is not a junk infimum, a contraction sequence cannot jump to one part in a single step, the error value counts non-constant zones per part on both sides, minors range over divisions with non-empty interval parts rather than arbitrary partitions, and all bounds depend only on ttt and α\alphaα, never on MMM.

A complete development needs: finite partitions and their merges, divisions as interval partitions, the Marcus–Tardos theorem, and bookkeeping for sequences of partitions. The partition machinery and Theorem 5.3 are reusable beyond this mission (graph twin-width in the other missions of this series, pattern avoidance). Proofs of individual milestones, and of the Marcus–Tardos theorem from its original argument, are welcome.

Selected references

  • É. Bonnet, E. J. Kim, S. Thomassé, R. Watrigant, Twin-width I: Tractable FO Model Checking, J. ACM 69(1), Article 3, 2021. https://doi.org/10.1145/3486655
  • A. Marcus, G. Tardos, Excluded permutation matrices and the Stanley–Wilf conjecture, J. Combin. Theory Ser. A 107(1), 153–160, 2004. https://doi.org/10.1016/j.jcta.2004.04.002
  • J. Fox, Stanley–Wilf limits are typically exponential, arXiv:1310.8378, 2013. https://arxiv.org/abs/1310.8378
  • J. Cibulka, J. Kynčl, Füredi–Hajnal limits are typically subexponential, arXiv:1607.07491, 2016. https://arxiv.org/abs/1607.07491
  • S. Guillemot, D. Marx, Finding small patterns in permutations in linear time, SODA 2014, 82–101. https://doi.org/10.1137/1.9781611973402.7
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