The Influence of Three Statistical Variables on Self-Similarity in Complex Networks
The reason for the self-similarity property of complex network is still an open issue. In this paper, we focus on the influence of degree, betweenness, and coreness on self-similarity of complex network. Some nodes are removed from the original network based on the definitions of degree, betweenness...
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Format: | Article |
Language: | English |
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Wiley
2020-01-01
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Series: | Discrete Dynamics in Nature and Society |
Online Access: | http://dx.doi.org/10.1155/2020/7860126 |
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author | Mingli Lei Lirong Liu Daijun Wei |
author_facet | Mingli Lei Lirong Liu Daijun Wei |
author_sort | Mingli Lei |
collection | DOAJ |
description | The reason for the self-similarity property of complex network is still an open issue. In this paper, we focus on the influence of degree, betweenness, and coreness on self-similarity of complex network. Some nodes are removed from the original network based on the definitions of degree, betweenness, and coreness in the ascending and descending order. And then, some new networks are obtained after removing nodes. The self-similarities of original network and new networks are compared. Moreover, two real networks are used for numerical simulation, including a USAir network and the yeast protein interaction (YPI) network. The effects of the three statistical variables on the two real networks are considered. The results reveal that the nodes with large degree and betweenness have great effects on self-similarity, and the influence of coreness on self-similarity is small. |
format | Article |
id | doaj-art-b0e5c8a8a6414e4988d9d57ba83a8c9c |
institution | Kabale University |
issn | 1026-0226 1607-887X |
language | English |
publishDate | 2020-01-01 |
publisher | Wiley |
record_format | Article |
series | Discrete Dynamics in Nature and Society |
spelling | doaj-art-b0e5c8a8a6414e4988d9d57ba83a8c9c2025-02-03T05:44:12ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/78601267860126The Influence of Three Statistical Variables on Self-Similarity in Complex NetworksMingli Lei0Lirong Liu1Daijun Wei2School of Science, Hubei University for Nationalities, Enshi 445000, ChinaSchool of Science, Hubei University for Nationalities, Enshi 445000, ChinaSchool of Science, Hubei University for Nationalities, Enshi 445000, ChinaThe reason for the self-similarity property of complex network is still an open issue. In this paper, we focus on the influence of degree, betweenness, and coreness on self-similarity of complex network. Some nodes are removed from the original network based on the definitions of degree, betweenness, and coreness in the ascending and descending order. And then, some new networks are obtained after removing nodes. The self-similarities of original network and new networks are compared. Moreover, two real networks are used for numerical simulation, including a USAir network and the yeast protein interaction (YPI) network. The effects of the three statistical variables on the two real networks are considered. The results reveal that the nodes with large degree and betweenness have great effects on self-similarity, and the influence of coreness on self-similarity is small.http://dx.doi.org/10.1155/2020/7860126 |
spellingShingle | Mingli Lei Lirong Liu Daijun Wei The Influence of Three Statistical Variables on Self-Similarity in Complex Networks Discrete Dynamics in Nature and Society |
title | The Influence of Three Statistical Variables on Self-Similarity in Complex Networks |
title_full | The Influence of Three Statistical Variables on Self-Similarity in Complex Networks |
title_fullStr | The Influence of Three Statistical Variables on Self-Similarity in Complex Networks |
title_full_unstemmed | The Influence of Three Statistical Variables on Self-Similarity in Complex Networks |
title_short | The Influence of Three Statistical Variables on Self-Similarity in Complex Networks |
title_sort | influence of three statistical variables on self similarity in complex networks |
url | http://dx.doi.org/10.1155/2020/7860126 |
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