Journal of Systems Science and Complexity

, Volume 23, Issue 5, pp 931–941 | Cite as

Self-organizing map of complex networks for community detection

  • Zhenping LiEmail author
  • Ruisheng Wang
  • Xiang-Sun Zhang
  • Luonan Chen


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.

Key words

Community detection complex network neural networks self-organizing map 


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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zhenping Li
    • 1
    Email author
  • Ruisheng Wang
    • 2
  • Xiang-Sun Zhang
    • 3
  • Luonan Chen
    • 4
  1. 1.School of InformationBeijing Wuzi UniversityBeijingChina
  2. 2.School of InformationRenmin University of ChinaBeijingChina
  3. 3.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  4. 4.Department of Electrical Engineering and ElectronicsOsaka Sangyo UniversityOsakaJapan

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