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Given an infinite connected graph $G$ of bounded degree with vertex set $X$, let $P_x^n$ the time $n$ distribution of the simple random walk started at the vertex $x$ (so $P^n_x(y)$ is the probability that a simple random walk started at $x$ ends at $y$ after $n$ steps). Let further $$ H_n :=\displaystyle \sup_{x \in X} h(P_x^n), \qquad \eta_n := \displaystyle \inf_{x \in X} h(P_x^n) \quad \text{ and } \quad r_n := \displaystyle \sup_{x,y \in X} P_x^n(y). $$ Here $h(\mu) = \sum_y -\mu(y) \ln \mu(y)$ is the entropy of $\mu$. Given that for any measure $\mu$ one has $\displaystyle \sup_{y \in X} \mu(y) \geq e^{-h(\mu)}$, one gets the bound $r_n \geq e^{-H_n}$. One question is:

Question 1: what are upper bounds on $r_n$ in terms of $H_n$ and $h_n$?

Note that in the case of [Cayley graphs of] groups, there is such a bound (the sharpest version known to me is by Peres & Zheng). My question is for "generic" [infinite connected] graphs [of bounded degree].

There is a natural counterpart to the question (which is hopefully easier to get):

Question 2: what are [family of] examples where upper bounds of $r_n$ in terms of $H_n$ and $\eta_n$ are "bad"?

Of course "bad" is not well-defined, but in Cayley graphs it may happen that $r_n$ behaves roughly like $C_1e^{-C_2\sqrt{H_n}}$. I would expect much worse for a graph.

PS: if "generic" needed to be made explicit, then I would say something like: "no finitely generated subgroup of the subgroup of self quasi-isometries of the graph acts co-compactly".

PPS: any result with "lazy random walk" in place of "simple random walk" is also OK.

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  • $\begingroup$ Curious if you can point to unconditional upper bounds on $r_n$? I'm wondering if one could modify one of those examples to have arbitrarily high $H_n$. $\endgroup$
    – usul
    Commented Apr 28, 2022 at 17:54
  • $\begingroup$ @usul Yuval Peres mentions below a very good example where $H_n$ is as high as one can hope for while $r_n$ is nearly as low as one can get. This is because the entropy is very high on the tree part, but the return probability is very high on the line. But as he mentions in his comment, $\eta_n$ is the quantity to look for in order to get an upper bound on $r_n$. $\endgroup$
    – ARG
    Commented Apr 28, 2022 at 19:04

2 Answers 2

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This is a very partial answer. For simple random walks on Cayley graphs, linear growth of $H_n$ implies that for any $\epsilon>0$, the inequality $r_n \le \exp((\epsilon-1/2)n)$ must hold for infinitely many $n$, see Theorem 1.1 in [1].

For infinite connected graphs of bounded degree, it is possible for $H_n$ to grow linearly while $r_n \asymp cn^{-3/2}$ for even $n$. A simple example is a graph $G$ obtained by adjoining an infinite path to the root of an infinite binary tree. I suspect that slower decay of $r_n$ is not compatible with linear growth of entropy.

(Edit: @tmh Indicated in a comment below that one could get close to decay rate of $1/n$ for $r_n$. So perhaps $1/n$ is a barrier? recall that without any entropy assumptions, the slowest possible decay rate for $r_n$ on an infinite graph of bounded degree is $\;$ const.$/\sqrt{n}$.)

[1] Peres, Yuval, and Tianyi Zheng. "On groups, slow heat kernel decay yields liouville property and sharp entropy bounds." International Mathematics Research Notices 2020, no. 3 (2020): 722-750. https://arxiv.org/abs/1609.05174

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  • $\begingroup$ thanks for the answer! I already mentioned your paper with Tianyi Zheng in my question; but the example of the tree with a ray adjoined is definitively what I was looking for. $\endgroup$
    – ARG
    Commented Apr 28, 2022 at 17:46
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    $\begingroup$ Assuming $\eta_n$ grows quickly should have better implications, using the expansion profile. I hope to add some more on that later. $\endgroup$ Commented Apr 28, 2022 at 17:49
  • $\begingroup$ $\eta_n$ should indeed be the more natural candidate for the upper bound on $r_n$. What is the expansion profile? (I could not find references to it...) $\endgroup$
    – ARG
    Commented Apr 28, 2022 at 18:53
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    $\begingroup$ If you adjoin a copy of Z^2 instead of a line to the tree the return probability decays like 1/n log n. I guess by adjoining a spherically symmetric tree with slightly superquadratic growth one can make the return probability decay arbitrarily close to 1/n. $\endgroup$
    – tmh
    Commented Apr 28, 2022 at 20:49
  • $\begingroup$ @ARG You were the first to go in this direction, so I am glad you are returning to it. One approach to deriving heat kernel bounds from expansion (=isoperimetric) profiles, is in link.springer.com/article/10.1007/s00440-005-0434-7 but there are many earlier and later papers on this theme by Grigoryan, Coulhon, etc. $\endgroup$ Commented Apr 28, 2022 at 22:47
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Too long for a comment, but I wanted to mention that we encountered some related issues in my recent preprint with Noah Halberstam https://arxiv.org/abs/2203.01540 and thought that the work-arounds we found might be useful to you too.

We would have liked to have argued that if $d(X_0,X_n)$ grows super-diffusively then the Greens function $G(X_0,X_n)$ decays quickly, but this does not seem to be true in general. (Actually we did not find a counterexample, although on page 23 we discuss some constructions related to the comment I wrote, where one has positive speed but slow heat kernel decay.)

For our purposes, we were able to avoid this problem with a nice trick: Introducing spatially-dependent killing to the random walk. If the walk is superdiffusive and one makes the rate of killing decay at a well-chosen rate as a function of the distance from the starting point then return probabilities on the killed network decay quickly but the walk has positive probability to never be killed (see Section 3 of our paper). As such, if you want to use heat kernel estimates to prove some almost-sure property of the random walk, you can run the argument on the network with the spatially-dependent killing to get that the property holds almost surely for the killed walk, then deduce that it also holds almost surely for the original walk by absolute continuity.

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    $\begingroup$ many thanks for your insights! $\endgroup$
    – ARG
    Commented May 1, 2022 at 18:54

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