Random walks on complex networks under node-dependent stochastic resetting

Abstract

In the present work, we study random walks on complex networks subject to stochastic resetting when the resetting probability is node-dependent. Using a renewal approach, we derive the exact expressions of the stationary occupation probabilities of the walker on each node and the mean first passage time between arbitrary two nodes. Finally, we demonstrate our theoretical results on three networks with two different resetting protocols, validated by numerical simulations as well. We find that under a delicate setting it is advantageous to optimize the efficiency of a global search on such networks by the node-dependent resetting probability.

10 pages, 3 figures

References in corpus (13)

• Understanding individual human mobility patterns
• Maps of random walks on complex networks reveal community structure
• Vital nodes identification in complex networks
• Reaction-diffusion processes and metapopulation models in heterogeneous networks
• First Passage Under Restart
• Diffusion in a potential landscape with stochastic resetting
• Optimal mean first-passage time for a Brownian searcher subjected to resetting: experimental and theoretical results
• Diffusion with resetting in arbitrary spatial dimension
• Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks
• Random walks on weighted networks
• Integral Fluctuation Theorems for Stochastic Resetting Systems
• Mean perimeter and area of the convex hull of a planar Brownian motion in the presence of resetting
• Random walks on complex networks with multiple resetting nodes: a renewal approach