Fast Mixing in Sparse Random Ising Models

Motivated by the community detection problem in Bayesian inference, as well as the recent explosion of interest in spin glasses from statistical physics, we study the classical Glauber dynamics for sampling from Ising models with sparse random interactions. It is now well-known that when the interaction matrix has spectral diameter less than 1, Glauber dynamics mixes in O (n n) steps. Unfortunately, such criteria fail dramatically for interactions supported on arguably the most well-studied sparse random graph: the Erdos--R\'enyi random graph G (n, d/n), due to the presence of almost linearly many outlier eigenvalues of unbounded magnitude. We prove that for the Viana--Bray spin glass, where the interactions are supported on G (n, d/n) and randomly assigned, Glauber dynamics mixes in n^1+o (1) time with high probability as long as O (1/d), independent of n. We further extend our results to random graphs drawn according to the 2-community stochastic block model, as well as when the interactions are given by a "centered" version of the adjacency matrix. The latter setting is particularly relevant for the inference problem in community detection. Indeed, we build on this result to demonstrate that Glauber dynamics succeeds at recovering communities in the stochastic block model in an upcoming paper. The primary technical ingredient in our proof is showing that with high probability, a sparse random graph can be decomposed into two parts --- a bulk which behaves like a graph with bounded maximum degree and a well-behaved spectrum, and a near-forest with favorable pseudorandom properties. We then use this decomposition to design a localization procedure that interpolates to simpler Ising models supported only on the near-forest, and then execute a pathwise analysis to establish a modified log-Sobolev inequality.