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Overlapping community detection using a generative model for networks

Author

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  • Wang, Zhenwen
  • Hu, Yanli
  • Xiao, Weidong
  • Ge, Bin

Abstract

Detecting overlapping communities is a challenging task in analyzing networks, where nodes may belong to more than one community. Many present methods optimize quality functions to extract the communities from a network. In this paper, we present a probabilistic method for detecting overlapping communities using a generative model. The model describes the probability of generating a network with the model parameters, which reflect the communities in the network. The community memberships of each node are determined based on a probabilistic approach using those model parameters, whose values can be obtained by fitting the model to the network. This method has the advantage that the node participation degrees in each community are also computed. The proposed method is compared with some other community detection methods on both synthetic networks and real-world networks. The experiments show that this method is efficient at detecting overlapping communities and can provide better performance on the networks where a majority of nodes belong to more than one community.

Suggested Citation

  • Wang, Zhenwen & Hu, Yanli & Xiao, Weidong & Ge, Bin, 2013. "Overlapping community detection using a generative model for networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5218-5230.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:20:p:5218-5230
    DOI: 10.1016/j.physa.2013.06.038
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Zhang, Shihua & Wang, Rui-Sheng & Zhang, Xiang-Sun, 2007. "Identification of overlapping community structure in complex networks using fuzzy c-means clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 483-490.
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