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Theorem 17.17 -- return probabilities of the lazy walk

Proved
MarkovMixing.lazy_return_probability_connected

by Shuze Chen · Aug 22, 2026 · Mathlib c5ea003 (Lean v4.30.0)

markov-chainsmixing-timesprobability

Let GGG be a finite simple graph on a non-empty vertex set VVV, connected (any vertex is reachable from any other along edges) and with every degree positive. Write deg⁡(v)\deg(v)deg(v) for the number of neighbours of vvv, ∣E∣|E|∣E∣ for the number of edges, and Δ=max⁡vdeg⁡(v)\Delta=\max_v\deg(v)Δ=maxv​deg(v) for the maximal degree. Simple random walk on GGG jumps from xxx to a uniformly chosen neighbour, P(x,y)=deg⁡(x)−1P(x,y)=\deg(x)^{-1}P(x,y)=deg(x)−1 when x∼yx\sim yx∼y and 000 otherwise; its lazy version P~=12(I+P)\tilde P=\tfrac12(I+P)P~=21​(I+P) stays put with probability 12\tfrac1221​ at each step. On a connected graph the walk has the stationary distribution π(x)=deg⁡(x)/2∣E∣\pi(x)=\deg(x)/2|E|π(x)=deg(x)/2∣E∣.

The theorem (Theorem 17.17 of Levin–Peres–Wilmer) asserts that the return probabilities of the lazy walk converge to π\piπ at a rate controlled by the maximal degree alone: for every vertex xxx and every time t≥1t\ge1t≥1,

∣P~ t(x,x)−deg⁡(x)2∣E∣∣  ≤  2 Δ5/2t.\Bigl|\tilde P^{\,t}(x,x)-\frac{\deg(x)}{2|E|}\Bigr|\;\le\;\frac{\sqrt2\,\Delta^{5/2}}{\sqrt t}.​P~t(x,x)−2∣E∣deg(x)​​≤t​2​Δ5/2​.

The bound does not mention the size of the graph — only its maximal degree — which is what makes it useful for large sparse graphs, and it is proved by the evolving-set method of this mission rather than by any spectral estimate. (The dependence on Δ\DeltaΔ is not optimal; LPW remark that c1Δ/tc_1\Delta/\sqrt tc1​Δ/t​ also holds.)

A note on the connectedness hypothesis. LPW write π(x)\pi(x)π(x) for the stationary distribution of the walk, which on a connected graph is deg⁡(x)/2∣E∣\deg(x)/2|E|deg(x)/2∣E∣. The expression deg⁡(x)/2∣E∣\deg(x)/2|E|deg(x)/2∣E∣ is of course a perfectly good number on any graph, and that is exactly what makes the omission of connectedness a falsehood rather than a vacuity: on a disconnected graph the walk started at xxx never leaves the component of xxx, so its return probabilities converge to that component's stationary mass, which is strictly larger. On two disjoint edges every degree and the maximal degree equal 111 and ∣E∣=2|E|=2∣E∣=2; the lazy walk matrix is idempotent, so P~ t(x,x)=12\tilde P^{\,t}(x,x)=\tfrac12P~t(x,x)=21​ for every t≥1t\ge1t≥1 while deg⁡(x)/2∣E∣=14\deg(x)/2|E|=\tfrac14deg(x)/2∣E∣=41​, and at t=100t=100t=100 the claim would read 14≤2/10\tfrac14\le\sqrt2/1041​≤2​/10. Positivity of the degrees rules out isolated vertices but not disconnection; it is kept alongside connectedness because a connected graph may still be the single vertex, where all three of deg⁡(x)\deg(x)deg(x), Δ\DeltaΔ and ∣E∣|E|∣E∣ vanish.

Preamble
import Definitions.Def_mm_martingale
import Mathlib.Analysis.SpecialFunctions.Pow.Real
import Mathlib.Analysis.SpecialFunctions.Sqrt
Formal statement
namespace MarkovMixing

/-- **Theorem 17.17** (LPW): for the lazy random walk on a connected graph of
maximal degree `Δ`, the return probabilities satisfy
`|P^t(x,x) − π(x)| ≤ √2 Δ^{5/2} / √t`.

The graph is hypothesized connected.  LPW write `π(x)` for the stationary
distribution of the walk, which is `deg(x)/2|E|`; that identification needs
connectedness, since on a disconnected graph the walk started at `x` never
leaves the component of `x` and its return probabilities converge to the
component's stationary mass, which is strictly larger.  The formula
`deg(x)/2|E|` is of course defined for every graph, which is what makes the
omission a falsehood rather than a vacuity: on two disjoint edges every degree
and the maximal degree are `1` and `|E| = 2`, the lazy walk matrix is
idempotent, so `P^t(x,x) = 1/2` for all `t ≥ 1` while `deg(x)/2|E| = 1/4`, and
at `t = 100` the claim would read `1/4 ≤ √2/10`.  Positivity of the degrees
rules out isolated vertices but not disconnection; it is kept because
connectedness alone still admits the one-vertex graph, where every degree,
`Δ` and `|E|` vanish. -/
theorem lazy_return_probability_connected {V : Type*} [Fintype V] [DecidableEq V]
    [Nonempty V] (G : SimpleGraph V) [DecidableRel G.Adj]
    (hconn : G.Connected) (hdeg : ∀ v : V, 0 < G.degree v)
    (x : V) (t : ℕ) (ht : 0 < t) :
    |((lazy (graphWalk G)) ^ t) x x -
        (G.degree x : ℝ) / (2 * G.edgeFinset.card)| ≤
      Real.sqrt 2 * (G.maxDegree : ℝ) ^ ((5 : ℝ) / 2) / Real.sqrt t := by
  sorry

end MarkovMixing
Source
D. A. Levin, Y. Peres, E. L. Wilmer, Markov Chains and Mixing Times, AMS 2009, https://documents.epfl.ch/groups/i/ip/ipg/www/2013-2014/Random_Walks/markovmixing.pdf, Section 17.5, Theorem 17.17, Eq. (17.31), p. 239

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