Random walks on complex networks under time-dependent stochastic resetting

TL;DR

This paper analyzes how time-dependent stochastic resetting affects random walks on networks, providing exact solutions for stationary states and first passage times, and demonstrating potential advantages over constant resetting in search efficiency.

Contribution

It introduces exactly solvable models of time-dependent resetting protocols and derives analytical expressions for key quantities in network random walks.

Findings

Time-dependent resetting protocols can outperform constant resetting in search tasks.

Analytical solutions for stationary and first passage times are obtained for specific resetting functions.

Numerical simulations validate the theoretical results and demonstrate practical advantages.

Abstract

We study discrete-time random walks on networks subject to a time-dependent stochastic resetting, where the walker either hops randomly between neighboring nodes with a probability $1-\phi(a)$, or is reset to a given node with a complementary probability $\phi(a)$. The resetting probability $\phi(a)$ depends on the time $a$ since the last reset event (also called $a$ the age of the walker). Using the renewal approach and spectral decomposition of transition matrix, we formulize the stationary occupation probability of the walker at each node and the mean first passage time between arbitrary two nodes. Concretely, we consider that two different time-dependent resetting protocols that are both exactly solvable. One is that $\phi(a)$ is a step-shaped function of $a$ and the other one is that $\phi(a)$ is a rational function of $a$. We demonstrate the theoretical results on two different networks, also validated by numerical simulations, and find that the time-modulated resetting protocols can be more advantageous than the constant-probability resetting in accelerating the completion of a target search process.