Multifractal Analysis of Weighted Complex Networks Based on Degree Volume Dimension

In the research field of complex networks, the network nodes and edges are the fundamental indicators and often used as the preliminary step in the structural analysis of complex networks. In this paper, first, considering the differences in the node degree distribution, the traditional box-covering...

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Main Authors: Wei Zheng, Qianjing You, Zifeng Zhang, Hao Pan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8718645/
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spelling doaj-ef5ad656f12b4c58ba6e621fe6444a862021-03-29T23:35:47ZengIEEEIEEE Access2169-35362019-01-017685226852810.1109/ACCESS.2019.29178848718645Multifractal Analysis of Weighted Complex Networks Based on Degree Volume DimensionWei Zheng0Qianjing You1https://orcid.org/0000-0002-7074-1010Zifeng Zhang2https://orcid.org/0000-0002-3095-314XHao Pan3https://orcid.org/0000-0003-4561-3911School of Software, Nanchang Hangkong University, Nanchang, ChinaSchool of Software, Nanchang Hangkong University, Nanchang, ChinaSchool of Software, Nanchang Hangkong University, Nanchang, ChinaSchool of Software, Nanchang Hangkong University, Nanchang, ChinaIn the research field of complex networks, the network nodes and edges are the fundamental indicators and often used as the preliminary step in the structural analysis of complex networks. In this paper, first, considering the differences in the node degree distribution, the traditional box-covering algorithm is modified by regarding the number of nodes in the box as the node degree, it is proved that multifractal characteristics are the nature of weighted complex networks. Moreover, multifractal spectrums of eight different networks are obtained and the variations in three eigenvalues corresponding are further analyzed, by comparing the existing and modified algorithms. Finally, we can make the practical conclusion that the multifractal characteristics of weighted complex networks are affected by the differences in the node degree distribution.https://ieeexplore.ieee.org/document/8718645/Degree volume dimensionmultifractalmodified box-covering algorithmweighted complex networks
collection DOAJ
language English
format Article
sources DOAJ
author Wei Zheng
Qianjing You
Zifeng Zhang
Hao Pan
spellingShingle Wei Zheng
Qianjing You
Zifeng Zhang
Hao Pan
Multifractal Analysis of Weighted Complex Networks Based on Degree Volume Dimension
IEEE Access
Degree volume dimension
multifractal
modified box-covering algorithm
weighted complex networks
author_facet Wei Zheng
Qianjing You
Zifeng Zhang
Hao Pan
author_sort Wei Zheng
title Multifractal Analysis of Weighted Complex Networks Based on Degree Volume Dimension
title_short Multifractal Analysis of Weighted Complex Networks Based on Degree Volume Dimension
title_full Multifractal Analysis of Weighted Complex Networks Based on Degree Volume Dimension
title_fullStr Multifractal Analysis of Weighted Complex Networks Based on Degree Volume Dimension
title_full_unstemmed Multifractal Analysis of Weighted Complex Networks Based on Degree Volume Dimension
title_sort multifractal analysis of weighted complex networks based on degree volume dimension
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description In the research field of complex networks, the network nodes and edges are the fundamental indicators and often used as the preliminary step in the structural analysis of complex networks. In this paper, first, considering the differences in the node degree distribution, the traditional box-covering algorithm is modified by regarding the number of nodes in the box as the node degree, it is proved that multifractal characteristics are the nature of weighted complex networks. Moreover, multifractal spectrums of eight different networks are obtained and the variations in three eigenvalues corresponding are further analyzed, by comparing the existing and modified algorithms. Finally, we can make the practical conclusion that the multifractal characteristics of weighted complex networks are affected by the differences in the node degree distribution.
topic Degree volume dimension
multifractal
modified box-covering algorithm
weighted complex networks
url https://ieeexplore.ieee.org/document/8718645/
work_keys_str_mv AT weizheng multifractalanalysisofweightedcomplexnetworksbasedondegreevolumedimension
AT qianjingyou multifractalanalysisofweightedcomplexnetworksbasedondegreevolumedimension
AT zifengzhang multifractalanalysisofweightedcomplexnetworksbasedondegreevolumedimension
AT haopan multifractalanalysisofweightedcomplexnetworksbasedondegreevolumedimension
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