Determination of multifractal dimensions of complex networks by means of the sandbox algorithm

TL;DR

This paper evaluates the sandbox algorithm for multifractal analysis of complex networks, compares it with other methods, and applies it to various network models, revealing multifractality in scale-free networks but not in random ones.

Contribution

The paper demonstrates that the sandbox algorithm is more effective for multifractal analysis of complex networks than existing methods.

Findings

Sandbox algorithm outperforms other MFA algorithms in accuracy and feasibility.

Multifractality is present in scale-free networks but not in random networks.

Small-world networks show weak or no multifractal behavior.

Abstract

Complex networks have attracted much attention in diverse areas of science and technology. Multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. In this paper, we employ the sandbox (SB) algorithm proposed by T\'{e}l et al. (Physica A, 159 (1989) 155-166), for MFA of complex networks. First we compare the SB algorithm with two existing algorithms of MFA for complex networks: the compact-box-burning (CBB) algorithm proposed by Furuya and Yakubo (Phys. Rev. E, 84 (2011) 036118), and the improved box-counting (BC) algorithm proposed by Li et al. (J. Stat. Mech.: Theor. Exp., 2014 (2014) P02020) by calculating the mass exponents tau(q) of some deterministic model networks. We make a detailed comparison between the numerical and theoretical results of these model networks. The comparison results show that the SB algorithm is the most effective and feasible algorithm to calculate the mass exponents tau(q) and to explore the multifractal behavior of complex networks. Then we apply the SB algorithm to study the multifractal property of some classic model networks, such as scale-free networks, small-world networks, and random networks. Our results show that multifractality exists in scale-free networks, that of small-world networks is not obvious, and it almost does not exist in random networks.