Abstract
In this paper, we consider the problem of sampling from the low-temperature exponential random graph model (ERGM). The usual approach is via Markov chain Monte Carlo, but Bhamidi et al. showed that any local Markov chain suffers from an exponentially large mixing time due to metastable states. We instead consider metastable mixing, a notion of approximate mixing relative to the stationary distribution, for which it turns out to suffice to mix only within a collection of metastable states. We show that the Glauber dynamics for the ERGM at any temperature—except at a lower-dimensional critical set of parameters—when initialized at for the right choice of p has a metastable mixing time of to within total variation distance .
Funding Statement
This work was supported in part by NSF CAREER award CCF-1940205 and NSF Award DMS-2022448.
Acknowledgments
G.B. and D.N. gratefully acknowledge the hospitality of the Simons Institute for Theoretical Computer Science during Fall 2020 and Fall 2021.
Citation
Guy Bresler. Dheeraj Nagaraj. Eshaan Nichani. "Metastable mixing of Markov chains: Efficiently sampling low temperature exponential random graphs." Ann. Appl. Probab. 34 (1A) 517 - 554, February 2024. https://doi.org/10.1214/23-AAP1971
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