Random reversible Markov matrices with tunable extremal eigenvalues

TL;DR

This paper introduces a method to generate large Markov matrices with tunable spectral properties by perturbing Erdős-Rényi graphs, enabling control over spectral gaps and eigenvalue distributions.

Contribution

It provides a novel construction of random Markov matrices with adjustable extremal eigenvalues and spectral gaps, expanding the toolkit for spectral graph analysis.

Findings

ESD converges to a symmetric nondegenerate distribution

Extremal eigenvalues are bounded within specific intervals

Spectral gap approaches a tunable limit

Abstract

Random sampling of large Markov matrices with a tunable spectral gap, a nonuniform stationary distribution, and a nondegenerate limiting empirical spectral distribution (ESD) is useful. Fix $c>0$ and $p>0$. Let $A_n$ be the adjacency matrix of a random graph following $\mathrm{G}(n, p/n)$, known as the Erd\H{o}s-R\'enyi distribution. Add $c/n$ to each entry of $A_n$ and then normalize its rows. It is shown that the resulting Markov matrix has the desired properties. Its ESD weakly converges in probability to a symmetric nondegenerate distribution, and its extremal eigenvalues, other than 1, fall in $[-1/\sqrt{1+c/k},-b]\cup [b,1/\sqrt{1+c/k}]$ for any $0< b < 1/\sqrt{1+c}$, where $k = \lfloor p \rfloor + 1$. Thus, for $p\in (0,1)$, the spectral gap tends to $1-1/\sqrt{1+c}$.