Theorem 17.17 -- return probabilities of the lazy walk
ProvedMarkovMixing.lazy_return_probability_connectedLet be a finite simple graph on a non-empty vertex set , connected (any vertex is reachable from any other along edges) and with every degree positive. Write for the number of neighbours of , for the number of edges, and for the maximal degree. Simple random walk on jumps from to a uniformly chosen neighbour, when and otherwise; its lazy version stays put with probability at each step. On a connected graph the walk has the stationary distribution .
The theorem (Theorem 17.17 of Levin–Peres–Wilmer) asserts that the return probabilities of the lazy walk converge to at a rate controlled by the maximal degree alone: for every vertex and every time ,
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 is not optimal; LPW remark that also holds.)
A note on the connectedness hypothesis. LPW write for the stationary distribution of the walk, which on a connected graph is . The expression 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 never leaves the component of , 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 and ; the lazy walk matrix is idempotent, so for every while , and at the claim would read . 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 , and vanish.
import Definitions.Def_mm_martingale import Mathlib.Analysis.SpecialFunctions.Pow.Real import Mathlib.Analysis.SpecialFunctions.Sqrt
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