Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet
Community detection has become a hot topic in complex networks. It plays an important role in information recommendation and public opinion control. Bipartite network, as a special complex network, reflects the characteristics of a kind of network in our life truly and objectively. Therefore, detect...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8755979/ |
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| author | Furong Chang Bofeng Zhang Yue Zhao Songxian Wu Kenji Yoshigoe |
| author_facet | Furong Chang Bofeng Zhang Yue Zhao Songxian Wu Kenji Yoshigoe |
| author_sort | Furong Chang |
| collection | DOAJ |
| description | Community detection has become a hot topic in complex networks. It plays an important role in information recommendation and public opinion control. Bipartite network, as a special complex network, reflects the characteristics of a kind of network in our life truly and objectively. Therefore, detecting community structure in bipartite networks is of great significance and has practical value. In this paper, we first introduce two concepts: a) a micro-bipartite network model Bi-EgoNet which can be used to analyze bipartite network from a micro view to reduce the complexity of structure in bipartite networks, and b) a complete bipartite graph that is a special bipartite graph with the indivisible property. Then, we propose a novel overlapping community detection algorithm based on a complete bipartite graph in micro-bipartite network Bi-EgoNet (CBG&BEN), which combines advantages of both a complete bipartite graph and Bi-EgoNet to get an optimal community structure. The CBG&BEN is evaluated on accuracy and effectiveness in several synthetic and real-world bipartite networks. The CBG&BEN is compared with other excellent existing algorithms, and our experimental results demonstrated that CBG&BEN is better at detecting overlapping community structure in bipartite networks. |
| format | Article |
| id | doaj-art-4eade14d433d463995778c09ee24a5f9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4eade14d433d463995778c09ee24a5f92025-08-20T02:51:18ZengIEEEIEEE Access2169-35362019-01-017914889149810.1109/ACCESS.2019.29269878755979Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-EgonetFurong Chang0https://orcid.org/0000-0002-1558-4120Bofeng Zhang1Yue Zhao2Songxian Wu3Kenji Yoshigoe4School of Computer Engineering and Science, Shanghai University, Shanghai, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaSchool of Mathematics and Statistics, Kashi University, Xinjiang, ChinaFaculty of Information Networking for Innovation and Design (INIAD), Toyo University, Tokyo, JapanCommunity detection has become a hot topic in complex networks. It plays an important role in information recommendation and public opinion control. Bipartite network, as a special complex network, reflects the characteristics of a kind of network in our life truly and objectively. Therefore, detecting community structure in bipartite networks is of great significance and has practical value. In this paper, we first introduce two concepts: a) a micro-bipartite network model Bi-EgoNet which can be used to analyze bipartite network from a micro view to reduce the complexity of structure in bipartite networks, and b) a complete bipartite graph that is a special bipartite graph with the indivisible property. Then, we propose a novel overlapping community detection algorithm based on a complete bipartite graph in micro-bipartite network Bi-EgoNet (CBG&BEN), which combines advantages of both a complete bipartite graph and Bi-EgoNet to get an optimal community structure. The CBG&BEN is evaluated on accuracy and effectiveness in several synthetic and real-world bipartite networks. The CBG&BEN is compared with other excellent existing algorithms, and our experimental results demonstrated that CBG&BEN is better at detecting overlapping community structure in bipartite networks.https://ieeexplore.ieee.org/document/8755979/Bipartite networkscomplete bipartite graphoverlapping communitycomplex network |
| spellingShingle | Furong Chang Bofeng Zhang Yue Zhao Songxian Wu Kenji Yoshigoe Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet IEEE Access Bipartite networks complete bipartite graph overlapping community complex network |
| title | Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet |
| title_full | Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet |
| title_fullStr | Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet |
| title_full_unstemmed | Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet |
| title_short | Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet |
| title_sort | overlapping community detecting based on complete bipartite graphs in micro bipartite network bi egonet |
| topic | Bipartite networks complete bipartite graph overlapping community complex network |
| url | https://ieeexplore.ieee.org/document/8755979/ |
| work_keys_str_mv | AT furongchang overlappingcommunitydetectingbasedoncompletebipartitegraphsinmicrobipartitenetworkbiegonet AT bofengzhang overlappingcommunitydetectingbasedoncompletebipartitegraphsinmicrobipartitenetworkbiegonet AT yuezhao overlappingcommunitydetectingbasedoncompletebipartitegraphsinmicrobipartitenetworkbiegonet AT songxianwu overlappingcommunitydetectingbasedoncompletebipartitegraphsinmicrobipartitenetworkbiegonet AT kenjiyoshigoe overlappingcommunitydetectingbasedoncompletebipartitegraphsinmicrobipartitenetworkbiegonet |