Spectra of Random Stochastic Matrices and Relaxation in Complex Systems

TL;DR

This paper analyzes the spectra of large, sparse stochastic matrices on random graphs to understand relaxation times in complex systems, distinguishing between transport and local dynamics.

Contribution

It provides a method to compute spectra of stochastic matrices with arbitrary structure and weights, applicable in the thermodynamic limit and for large instances.

Findings

Spectra reveal relaxation time distributions in complex systems.

Method distinguishes between extended and localized states.

Results applicable to diverse stochastic processes.

Abstract

We compute spectra of large stochastic matrices $W$, defined on sparse random graphs, where edges $(i,j)$ of the graph are given positive random weights $W_{ij}>0$ in such a fashion that column sums are normalized to one. We compute spectra of such matrices both in the thermodynamic limit, and for single large instances. The structure of the graphs and the distribution of the non-zero edge weights $W_{ij}$ are largely arbitrary, as long as the mean vertex degree remains finite in the thermodynamic limit and the $W_{ij}$ satisfy a detailed balance condition. Knowing the spectra of stochastic matrices is tantamount to knowing the complete spectrum of relaxation times of stochastic processes described by them, so our results should have many interesting applications for the description of relaxation in complex systems. Our approach allows to disentangle contributions to the spectral density related to extended and localized states, respectively, allowing to differentiate between time-scales associated with transport processes and those associated with the dynamics of local rearrangements.