Standard random walks and trapping on the Koch network with scale-free behavior and small-world effect

TL;DR

This paper analytically studies random walks on a Koch network, revealing that the mean first-passage time scales linearly with network size, and demonstrates the network's scale-free and small-world properties.

Contribution

It introduces a new iterative algorithm to generate the Koch network and derives exact formulas for random walk dynamics on it, linking topology to transport efficiency.

Findings

MFPT scales linearly with network size

Koch network exhibits scale-free and small-world features

Analytical results confirmed by numerical simulations

Abstract

A vast variety of real-life networks display the ubiquitous presence of scale-free phenomenon and small-world effect, both of which play a significant role in the dynamical processes running on networks. Although various dynamical processes have been investigated in scale-free small-world networks, analytical research about random walks on such networks is much less. In this paper, we will study analytically the scaling of the mean first-passage time (MFPT) for random walks on scale-free small-world networks. To this end, we first map the classical Koch fractal to a network, called Koch network. According to this proposed mapping, we present an iterative algorithm for generating the Koch network, based on which we derive closed-form expressions for the relevant topological features, such as degree distribution, clustering coefficient, average path length, and degree correlations. The obtained solutions show that the Koch network exhibits scale-free behavior and small-world effect. Then, we investigate the standard random walks and trapping issue on the Koch network. Through the recurrence relations derived from the structure of the Koch network, we obtain the exact scaling for the MFPT. We show that in the infinite network order limit, the MFPT grows linearly with the number of all nodes in the network. The obtained analytical results are corroborated by direct extensive numerical calculations. In addition, we also determine the scaling efficiency exponents characterizing random walks on the Koch network.