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Finding overlapping communities using seed set

Author

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  • Yang, Jin-Xuan
  • Zhang, Xiao-Dong

Abstract

The local optimization algorithm using seed set to find overlapping communities has become more and more a significant method, but it is a great challenge how to choose a good seed set. In this paper, a new method is proposed to achieve the choice of candidate seed sets, and yields a new algorithm to find overlapping communities in complex networks. By testing in real world networks and synthetic networks, this method can successfully detect overlapping communities and outperform other state-of-the-art overlapping community detection methods.

Suggested Citation

  • Yang, Jin-Xuan & Zhang, Xiao-Dong, 2017. "Finding overlapping communities using seed set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 96-106.
  • Handle: RePEc:eee:phsmap:v:467:y:2017:i:c:p:96-106
    DOI: 10.1016/j.physa.2016.10.006
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    References listed on IDEAS

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    1. Wang, Wenjun & Liu, Dong & Liu, Xiao & Pan, Lin, 2013. "Fuzzy overlapping community detection based on local random walk and multidimensional scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6578-6586.
    2. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    3. Shen, Huawei & Cheng, Xueqi & Cai, Kai & Hu, Mao-Bin, 2009. "Detect overlapping and hierarchical community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1706-1712.
    4. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    5. Li, Junqiu & Wang, Xingyuan & Eustace, Justine, 2013. "Detecting overlapping communities by seed community in weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6125-6134.
    6. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.

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