Fractal Analysis of Mobile Social Networks

Zheng, Wei Xing; Pan, Qian; Sun, Chen; Deng, Yufan; Zhao, Kang

doi:10.1088/0256-307x/33/3/038901

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…In order to apply the overlapping box covering algorithm to the calculation of complex networks fractal dimension, we improve the algorithm basing on the ratio of excluded mass to closeness centrality [35]. Different central nodes affect the effective division of the box of the complex networks, and the node with the smallest ratio of the excluded mass to closeness centrality is selected as the central node of the network.…”

Section: Proposed Methods a The Improved Overlapping Box Coveringmentioning

confidence: 99%

“…(1) are shown in Table 2. The REMCC method has the determined position of the center of each box, which has been proved better than the MEMB algorithm [35]. Nevertheless, networks are divided into separate boxes and the optimal number of boxes cannot be obtained, and the time complexity of the algorithm is relatively high.…”

Section: B Methods Verification In Complex Networkmentioning

confidence: 99%

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Fractal Analysis of Overlapping Box Covering Algorithm for Complex Networks

et al. 2020

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Due to extensive research on complex networks, fractal analysis with scale invariance is applied to measure the topological structure and self-similarity of complex networks. Fractal dimension can be used to quantify the fractal properties of the complex networks. However, in the existing box covering algorithms, accurately calculating the fractal dimension of complex networks is still an NP-hard problem. Therefore, in this paper, an improved overlapping box covering algorithm is proposed to explore a more accurate and effective method to calculate the fractal dimension of complex networks. Moreover, in order to verify the effectiveness of the algorithm, the improved algorithm is applied to six complex networks, and compared with other algorithms. Finally, the experimental results demonstrate that the improved overlapping box covering algorithm can cover the whole networks with fewer boxes. In addition, the improved overlapping box covering algorithm is a high accuracy and low time complexity method for calculating the fractal dimension of complex networks. INDEX TERMS Overlapping box covering algorithm, fractal dimension, complex networks.

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Section: Proposed Methods a The Improved Overlapping Box Coveringmentioning

confidence: 99%

Section: B Methods Verification In Complex Networkmentioning

confidence: 99%

Fractal Analysis of Overlapping Box Covering Algorithm for Complex Networks

et al. 2020

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“…The REMCC box-covering algorithm has been introduced by Zheng et al (2016), and it can be regarded as a modification of the MEMB algorithm. Both algorithms rely on excluded mass, but it also considers the ratio of excluded mass to closeness centrality (REMCC) to select the center nodes.…”

Section: Ratio Of Excluded Mass To Closeness Centrality (Remcc)mentioning

confidence: 99%

Comparative analysis of box-covering algorithms for fractal networks

2021

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Research on fractal networks is a dynamically growing field of network science. A central issue is to analyze the fractality with the so-called box-covering method. As this problem is known to be NP-hard, a plethora of approximating algorithms have been proposed throughout the years. This study aims to establish a unified framework for comparing approximating box-covering algorithms by collecting, implementing, and evaluating these methods in various aspects including running time and approximation ability. This work might also serve as a reference for both researchers and practitioners, allowing fast selection from a rich collection of box-covering algorithms with a publicly available codebase.

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“…This method implies covering a network with a minimum number of boxes

N_b

of a given box size

l_b

. When the number of boxes

N_{b}(l_{b})

is distributed according to a power law (see Equation ()), it is considered that

D

is the fractal dimension of a complex network [8–11].

\begin{eqnarray} N_{b}(l_{b})\sim l_{b}^{-D}.

…”

Section: Introductionmentioning

confidence: 99%

Centre including eccentricity algorithm for complex networks

Akhtanov

Turlykozhayeva

Ussipov

et al. 2022

Electronics Letters

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In this work, the authors propose a new centre including eccentricity algorithm, to define the fractal dimension of networks. The authors did the fractal analysis of the real Escherichia coli network and a model UV-flower network and confirmed that these networks are fractals . The fractal dimensions (D) of these networks are calculated and D = 2.485 for the real E. coli network and D = 2.1 for the UV-flower network is obtained. Also, the authors defined the fractal dimensions of real social networks and compared their method with Song's, Zhang's and Zheng's methods. Furthermore, the authors' algorithm can solve situations with single-node boxes at the edges of the network and can cover networks with minimum number of boxes. The authors believe that centre including eccentricity algorithm is competitive among existing algorithms and can be used to evaluate fractal properties of complex networks.

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Fractal Analysis of Mobile Social Networks

Cited by 9 publications

References 18 publications

Fractal Analysis of Overlapping Box Covering Algorithm for Complex Networks

Fractal Analysis of Overlapping Box Covering Algorithm for Complex Networks

Comparative analysis of box-covering algorithms for fractal networks

Centre including eccentricity algorithm for complex networks

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