Trapping in scale-free networks with hierarchical organization of modularity
Zhongzhi Zhang, Yuan Lin, Shuyang Gao, Shuigeng Zhou, Jihong Guan, and, Mo Li

TL;DR
This paper analyzes how the combined scale-free and modular structure of hierarchical networks influences the efficiency of a trapping process, revealing a power-law growth of mean first-passage time that indicates high transport efficiency.
Contribution
It provides an explicit, exact formula for the mean first-passage time in hierarchical scale-free networks, highlighting their superior transport efficiency due to topology.
Findings
MFPT grows algebraically with network size, with an exponent less than 1.
Hierarchical networks facilitate more efficient diffusion trapping compared to other scale-free networks.
The topology's scale-free and modular features enhance transport efficiency.
Abstract
A wide variety of real-life networks share two remarkable generic topological properties: scale-free behavior and modular organization, and it is natural and important to study how these two features affect the dynamical processes taking place on such networks. In this paper, we investigate a simple stochastic process--trapping problem, a random walk with a perfect trap fixed at a given location, performed on a family of hierarchical networks that exhibit simultaneously striking scale-free and modular structure. We focus on a particular case with the immobile trap positioned at the hub node having the largest degree. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping problem, which is the mean of the node-to-trap first-passage time over the entire network. The exact expression for the MFPT is calculated through the…
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