Mixed random walks with a trap in scale-free networks including nearest-neighbor and next-nearest-neighbor jumps

TL;DR

This paper analyzes mixed random walks with both nearest and next-nearest neighbor jumps in fractal scale-free networks, deriving analytical expressions for trapping efficiency and eigenvalues, revealing how jumps influence the process.

Contribution

It provides the first analytical study of trapping times and eigenvalues for mixed random walks with next-nearest-neighbor jumps in fractal scale-free networks.

Findings

Next-nearest-neighbor jumps do not affect the scaling of trapping efficiency.

Jumps significantly influence the prefactor of the average trapping time.

Analytical expressions for eigenvalues of the fundamental matrix are derived.

Abstract

Random walks including non-nearest-neighbor jumps appear in many real situations such as the diffusion of adatoms and have found numerous applications including PageRank search algorithm, however, related theoretical results are much less for this dynamical process. In this paper, we present a study of mixed random walks in a family of fractal scale-free networks, where both nearest-neighbor and next-nearest-neighbor jumps are included. We focus on trapping problem in the network family, which is a particular case of random walks with a perfect trap fixed at the central high-degree node. We derive analytical expressions for the average trapping time (ATT), a quantitative indicator measuring the efficiency of the trapping process, by using two different methods, the results of which are consistent with each other. Furthermore, we analytically determine all the eigenvalues and their multiplicities for the fundamental matrix characterizing the dynamical process. Our results show that although next-nearest-neighbor jumps have no effect on the leading sacling of the trapping efficiency, they can strongly affect the prefactor of ATT, providing insight into better understanding of random-walk process in complex systems.