2008
DOI: 10.1007/s10959-008-0183-5
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Heat Kernel Estimates for Strongly Recurrent Random Walk on Random Media

Abstract: We establish general estimates for simple random walk on an arbitrary infinite random graph, assuming suitable bounds on volume and effective resistance for the graph. These are generalizations of the results in [6, Section 1,2], and in particular, imply the spectral dimension of the random graph. We will also give an application of the results to random walk on a long range percolation cluster. Key Words: Random walk -Random media -Heat kernel estimates -Spectral dimension -Long range percolation Running Head… Show more

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Cited by 65 publications
(125 citation statements)
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“…This will be done via an appeal to the following theorem of Barlow, Járai, Kumagai, and Slade [12], which gives a sufficient condition for Alexander-Orbach behaviour. See [12] for quantitative versions of the theorem, and [48] for generalizations. We recall that the effective conductance between two disjoint finite sets A, B in a finite network G is defined to be The estimate (8.1) has already been established in Corollary 6.3 and Lemma 6.11.…”
Section: Spectral Dimension Anomalous Diffusionmentioning
confidence: 99%
“…This will be done via an appeal to the following theorem of Barlow, Járai, Kumagai, and Slade [12], which gives a sufficient condition for Alexander-Orbach behaviour. See [12] for quantitative versions of the theorem, and [48] for generalizations. We recall that the effective conductance between two disjoint finite sets A, B in a finite network G is defined to be The estimate (8.1) has already been established in Corollary 6.3 and Lemma 6.11.…”
Section: Spectral Dimension Anomalous Diffusionmentioning
confidence: 99%
“…In this light, our abstract convergence theorem, Theorem 1.1, can be seen as a more delicate version of [47], which gave sufficient conditions to prove the Alexander-Orbach conjecture that were subsequently applied to prove that conjecture in the case of critical percolation (see [42,45] for a generalization).…”
Section: Discussing the Universality Of The Brownian Motion On The Ismentioning
confidence: 99%
“…Since in our case |E(G)| ≈ r 2 we get that the commute time is ≈ r 3 . Now, in general the commute time only bounds the hitting time Hit(0, ∂B IIC (0, r)) from above, but in strongly recurrent graphs this turns out to be sharp [39]. Thus, in time r 3 the random walk has walked only in B IIC (0, r) and it can be shown that the end point is approximately uniformly distributed (the walk has mixed in B IIC (0, r) in that time).…”
Section: Introductionmentioning
confidence: 99%