Social Network Community-Discovery Algorithm Based on a Balance Factor
Community discovery plays a crucial role in understanding the structure of networks. In recent years, the application of clustering algorithms in the community-discovery tasks of complex networks has been studied frequently. In this study, we proposed a balance factor of node density and node-degree...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/7518422 |
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| _version_ | 1832553068099534848 |
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| author | Lizhao Liu Xiaomei Shu Biao Cai |
| author_facet | Lizhao Liu Xiaomei Shu Biao Cai |
| author_sort | Lizhao Liu |
| collection | DOAJ |
| description | Community discovery plays a crucial role in understanding the structure of networks. In recent years, the application of clustering algorithms in the community-discovery tasks of complex networks has been studied frequently. In this study, we proposed a balance factor of node density and node-degree centrality for the core-node selection problem in community discovery. We also proposed a new community-discovery algorithm based on the balance factor, adaptability, and modularity increment, which is based on the balance factor (BComd). First, the proposed method was able to identify the core nodes in a community. Second, we used node-degree centrality, node density, and adaptability to detect overlaps between communities and then we removed these overlaps from the network to obtain a subnetwork with a clear community structure. Third, we obtained the preliminary community divisions by clustering the subnetworks, and these preliminary communities were usually the core parts of the communities they belonged to. Finally, each preliminary community was compressed into a new node, and then, the new network was clustered using the Louvain algorithm. The experimental results showed that the algorithm identified the core nodes in communities well, effectively discovered overlaps between communities, and had superior performance in large-scale networks. |
| format | Article |
| id | doaj-art-7d33b6e0a0fb481f80c65dbd152bab53 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-7d33b6e0a0fb481f80c65dbd152bab532025-02-03T05:57:09ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/7518422Social Network Community-Discovery Algorithm Based on a Balance FactorLizhao Liu0Xiaomei Shu1Biao Cai2School of Mechanical and Electrical EngineeringCollege of Computer ScienceCollege of Computer Science and Cyber SecurityCommunity discovery plays a crucial role in understanding the structure of networks. In recent years, the application of clustering algorithms in the community-discovery tasks of complex networks has been studied frequently. In this study, we proposed a balance factor of node density and node-degree centrality for the core-node selection problem in community discovery. We also proposed a new community-discovery algorithm based on the balance factor, adaptability, and modularity increment, which is based on the balance factor (BComd). First, the proposed method was able to identify the core nodes in a community. Second, we used node-degree centrality, node density, and adaptability to detect overlaps between communities and then we removed these overlaps from the network to obtain a subnetwork with a clear community structure. Third, we obtained the preliminary community divisions by clustering the subnetworks, and these preliminary communities were usually the core parts of the communities they belonged to. Finally, each preliminary community was compressed into a new node, and then, the new network was clustered using the Louvain algorithm. The experimental results showed that the algorithm identified the core nodes in communities well, effectively discovered overlaps between communities, and had superior performance in large-scale networks.http://dx.doi.org/10.1155/2022/7518422 |
| spellingShingle | Lizhao Liu Xiaomei Shu Biao Cai Social Network Community-Discovery Algorithm Based on a Balance Factor Discrete Dynamics in Nature and Society |
| title | Social Network Community-Discovery Algorithm Based on a Balance Factor |
| title_full | Social Network Community-Discovery Algorithm Based on a Balance Factor |
| title_fullStr | Social Network Community-Discovery Algorithm Based on a Balance Factor |
| title_full_unstemmed | Social Network Community-Discovery Algorithm Based on a Balance Factor |
| title_short | Social Network Community-Discovery Algorithm Based on a Balance Factor |
| title_sort | social network community discovery algorithm based on a balance factor |
| url | http://dx.doi.org/10.1155/2022/7518422 |
| work_keys_str_mv | AT lizhaoliu socialnetworkcommunitydiscoveryalgorithmbasedonabalancefactor AT xiaomeishu socialnetworkcommunitydiscoveryalgorithmbasedonabalancefactor AT biaocai socialnetworkcommunitydiscoveryalgorithmbasedonabalancefactor |