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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This research is supported by the National Natural Science Foundation of China under Grant Nos 10631070, 60873205, 10701080, and the Beijing Natural Science Foundation under Grant No. 1092011. It is also partially supported by the Foundation of Beijing Education Commission under Grant No. SM200910037005, the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (PHR201006217), and the Foundation of WYJD200902.
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Li, Z., Wang, R., Zhang, XS. et al. Self-organizing map of complex networks for community detection. J Syst Sci Complex 23, 931–941 (2010). https://doi.org/10.1007/s11424-010-0202-3
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DOI: https://doi.org/10.1007/s11424-010-0202-3