Self-organizing map of complex networks for community detection |
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Authors: | Zhenping Li Ruisheng Wang Xiang-Sun Zhang Luonan Chen |
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Affiliation: | 1.School of Information,Beijing Wuzi University,Beijing,China;2.School of Information,Renmin University of China,Beijing,China;3.Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing,China;4.Department of Electrical Engineering and Electronics,Osaka Sangyo University,Osaka,Japan |
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Abstract: | Detecting communities from complex networks is an important issue and has attracted attention of researchers in many fields. It is relevant to social tasks, biological inquiries, and technological problems since various networks exist in these systems. This paper proposes a new self-organizing map (SOM) based approach to community detection. By adopting a new operation and a new weight-updating scheme, a complex network can be organized into dense subgraphs according to the topological connection of each node by the SOM algorithm. Extensive numerical experiments show that the performance of the SOM algorithm is good. It can identify communities more accurately than existing methods. This method can be used to detect communities not only in undirected networks, but also in directed networks and bipartite networks. |
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