The mixing time of the switch Markov chains: a unified approach

TL;DR

This paper introduces a unified approach to analyze the rapid mixing of switch Markov chains across various graph degree sequence families, generalizing previous results and providing new applications.

Contribution

It unifies multiple existing methods into a single framework, proving rapid mixing for broad classes of degree sequences and deriving new results for power-law and Erdős-Rényi graphs.

Findings

Switch Markov chains are rapidly mixing on P-stable families of degree sequences.

The method applies to unconstrained, bipartite, and directed sequences.

New results include efficient sampling for power-law and Erdős-Rényi graphs.

Abstract

Since 1997 a considerable effort has been spent to study the mixing time of switch Markov chains on the realizations of graphic degree sequences of simple graphs. Several results were proved on rapidly mixing Markov chains on unconstrained, bipartite, and directed sequences, using different mechanisms. The aim of this paper is to unify these approaches. We will illustrate the strength of the unified method by showing that on any $P$-stable family of unconstrained/bipartite/directed degree sequences the switch Markov chain is rapidly mixing. This is a common generalization of every known result that shows the rapid mixing nature of the switch Markov chain on a region of degree sequences. Two applications of this general result will be presented. One is an almost uniform sampler for power-law degree sequences with exponent $\gamma>1+\sqrt{3}$. The other one shows that the switch Markov chain on the degree sequence of an Erd\H{o}s-R\'enyi random graph $G(n,p)$ is asymptotically almost surely rapidly mixing if $p$ is bounded away from 0 and 1 by at least $\frac{5\log n}{n-1}$.