Random walks on complex networks with first-passage resetting

TL;DR

This paper analyzes how first-passage resetting affects random walks on complex networks, deriving exact formulas for key metrics and demonstrating improved search efficiency through various network examples.

Contribution

It introduces a framework for random walks with first-passage resetting on arbitrary networks, providing exact analytical expressions for occupation probabilities and first-passage times.

Findings

Exact formulas for stationary occupation probabilities and mean first-passage times.

First-passage resetting enhances global search efficiency on complex networks.

Demonstrations on ring, lattice, barbell, and Cayley tree networks.

Abstract

We study discrete-time random walks on arbitrary networks with first-passage resetting processes. To the end, a set of nodes are chosen as observable nodes, and the walker is reset instantaneously to a given resetting node whenever it hits either of observable nodes. We derive exact expressions of the stationary occupation probability, the average number of resets in the long time, and the mean first-passage time between arbitrary two non-observable nodes. We show that all the quantities can be expressed in terms of the fundamental matrix $\textbf{Z}=(\textbf{I}-\textbf{Q})^{-1}$, where $\textbf{I}$ is the identity matrix and $\textbf{Q}$ is the transition matrix between non-observable nodes. Finally, we use ring networks, 2d square lattices, barbell networks, and Cayley trees to demonstrate the advantage of first-passage resetting in global search on such networks.