Role of fractal dimension in random walks on scale-free networks

Abstract

Fractal dimension is central to understanding dynamical processes occurring on networks; however, the relation between fractal dimension and random walks on fractal scale-free networks has been rarely addressed, despite the fact that such networks are ubiquitous in real-life world. In this paper, we study the trapping problem on two families of networks. The first is deterministic, often called $(x,y)$-flowers; the other is random, which is a combination of $(1,3)$-flower and $(2,4)$-flower and thus called hybrid networks. The two network families display rich behavior as observed in various real systems, as well as some unique topological properties not shared by other networks. We derive analytically the average trapping time for random walks on both the $(x,y)$-flowers and the hybrid networks with an immobile trap positioned at an initial node, i.e., a hub node with the highest degree in the networks. Based on these analytical formulae, we show how the average trapping time scales with the network size. Comparing the obtained results, we further uncover that fractal dimension plays a decisive role in the behavior of average trapping time on fractal scale-free networks, i.e., the average trapping time decreases with an increasing fractal dimension.

Definitive version published in European Physical Journal B

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• Multifractality in random networks with power-law decaying bond strengths
• Random walks in unweighted and weighted modular scale-free networks with a perfect trap
• Optimal scale-free network with a minimum scaling of transport efficiency for random walks with a perfect trap
• Spectra, hitting times, and resistance distances of $q$-subdivision graphs
• Effects of node position on diffusion and trapping efficiency for random walks on fractal scale-free trees
• Hub-collision avoidance and leaf-node options algorithm for fractal dimension and renormalization of complex networks