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Detecting community structure in complex networks based on a measure of information discrepancy

Author

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  • Zhang, Junhua
  • Zhang, Shihua
  • Zhang, Xiang-Sun

Abstract

Properties of complex networks, such as small-world property, power-law degree distribution, network transitivity, and network- community structure which seem to be common to many real-world networks have attracted great interest among researchers. In this study, global information of the networks is considered by defining the profile of any node based on the shortest paths between it and all the other nodes in the network; then a useful iterative procedure for community detection based on a measure of information discrepancy and the popular modular function Q is presented. The new iterative method does not need any prior knowledge about the community structure and can detect an appropriate number of communities, which can be hub communities or non-hub communities. The computational results of the method on real networks confirm its capability.

Suggested Citation

  • Zhang, Junhua & Zhang, Shihua & Zhang, Xiang-Sun, 2008. "Detecting community structure in complex networks based on a measure of information discrepancy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(7), pages 1675-1682.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:7:p:1675-1682
    DOI: 10.1016/j.physa.2007.10.061
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    Cited by:

    1. Lu, Hu & Wei, Hui, 2012. "Detection of community structure in networks based on community coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6156-6164.
    2. Liu, Jian & Liu, Tingzhan, 2010. "Detecting community structure in complex networks using simulated annealing with k-means algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2300-2309.

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