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Discovering natural communities in networks

Author

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  • Li, Angsheng
  • Li, Jiankou
  • Pan, Yicheng

Abstract

Understanding and detecting natural communities in networks have been a fundamental challenge in networks, and in science generally. Recently, we proposed a hypothesis that homophyly/kinship is the principle of natural communities based on real network experiments, proposed a model of networks to explore the principle of natural selection in nature evolving, and proposed the measure of structure entropy of networks. Here we proposed a community finding algorithm by our measure of structure entropy of networks. We found that our community finding algorithm exactly identifies almost all natural communities of networks generated by natural selection, if any, and that the algorithm exactly identifies or precisely approximates almost all the communities planted in the networks of the existing models. We verified that our algorithm identifies or very well approximates the ground-truth communities of some real world networks, if the ground-truth communities are semantically well-defined, that our algorithm naturally finds the balanced communities, and that the communities found by our algorithm may have larger modularity than that by the algorithms based on modularity, for some networks. Our algorithm provides for the first time an approach to detecting and analyzing natural or true communities in real world networks. Our results demonstrate that structure entropy minimization is the principle of detecting the natural or true communities in large-scale networks.

Suggested Citation

  • Li, Angsheng & Li, Jiankou & Pan, Yicheng, 2015. "Discovering natural communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 878-896.
  • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:878-896
    DOI: 10.1016/j.physa.2015.05.039
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    References listed on IDEAS

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    1. Sanjeev Goyal & Marco J. van der Leij & José Luis Moraga-Gonzalez, 2006. "Economics: An Emerging Small World," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 403-432, April.
    2. Li, Angsheng & Li, Jiankou & Pan, Yicheng, 2015. "Homophyly/kinship hypothesis: Natural communities, and predicting in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 148-163.
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