Path statistics, memory, and coarse-graining of continuous-time random walks on networks

TL;DR

This paper develops a statistical mechanics framework to analyze continuous-time random walks on networks, accounting for memory, non-exponential waiting times, and coarse-graining, with an efficient algorithm implemented in Python.

Contribution

It introduces a novel approach for calculating path statistics in CTRWs on complex networks, including higher moments and effects of memory and coarse-graining.

Findings

Exact relations between path length and time moments for homogeneous networks

Recursion relations for efficient numerical calculation of path statistics

Demonstration of the importance of non-exponential waiting times and memory effects

Abstract

Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states, for example those employed in studies of molecular kinetics, and models with spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. We therefore use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach is valuable for calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN, a Python script that users can easily apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.