Community Detection by Node Betweenness and Similarity in Complex Network
Community detection of complex networks has always been a hot issue. With the mixed parameters μ increase in network complexity, community detection algorithms need to be improved. Based on previous work, the paper designs a novel algorithm from the perspective of node betweenness properties and giv...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9986895 |
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| _version_ | 1832548917902835712 |
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| author | Chong Feng Jianxu Ye Jianlu Hu Hui Lin Yuan |
| author_facet | Chong Feng Jianxu Ye Jianlu Hu Hui Lin Yuan |
| author_sort | Chong Feng |
| collection | DOAJ |
| description | Community detection of complex networks has always been a hot issue. With the mixed parameters μ increase in network complexity, community detection algorithms need to be improved. Based on previous work, the paper designs a novel algorithm from the perspective of node betweenness properties and gives the detailed steps of the algorithm and simulation results. We compare the proposed algorithm with a series of typical algorithms through experiments on synthetic and actual networks. Experimental results on artificial and real networks demonstrate the effectiveness and superiority of our algorithm. |
| format | Article |
| id | doaj-art-c7eac5d55d8a4ab9befa2e980a7ba21d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c7eac5d55d8a4ab9befa2e980a7ba21d2025-02-03T06:12:50ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99868959986895Community Detection by Node Betweenness and Similarity in Complex NetworkChong Feng0Jianxu Ye1Jianlu Hu2Hui Lin Yuan3College of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Management, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaCommunity detection of complex networks has always been a hot issue. With the mixed parameters μ increase in network complexity, community detection algorithms need to be improved. Based on previous work, the paper designs a novel algorithm from the perspective of node betweenness properties and gives the detailed steps of the algorithm and simulation results. We compare the proposed algorithm with a series of typical algorithms through experiments on synthetic and actual networks. Experimental results on artificial and real networks demonstrate the effectiveness and superiority of our algorithm.http://dx.doi.org/10.1155/2021/9986895 |
| spellingShingle | Chong Feng Jianxu Ye Jianlu Hu Hui Lin Yuan Community Detection by Node Betweenness and Similarity in Complex Network Complexity |
| title | Community Detection by Node Betweenness and Similarity in Complex Network |
| title_full | Community Detection by Node Betweenness and Similarity in Complex Network |
| title_fullStr | Community Detection by Node Betweenness and Similarity in Complex Network |
| title_full_unstemmed | Community Detection by Node Betweenness and Similarity in Complex Network |
| title_short | Community Detection by Node Betweenness and Similarity in Complex Network |
| title_sort | community detection by node betweenness and similarity in complex network |
| url | http://dx.doi.org/10.1155/2021/9986895 |
| work_keys_str_mv | AT chongfeng communitydetectionbynodebetweennessandsimilarityincomplexnetwork AT jianxuye communitydetectionbynodebetweennessandsimilarityincomplexnetwork AT jianluhu communitydetectionbynodebetweennessandsimilarityincomplexnetwork AT huilinyuan communitydetectionbynodebetweennessandsimilarityincomplexnetwork |