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Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks

Author

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  • Cui, Yaozu
  • Wang, Xingyuan
  • Eustace, Justine

Abstract

Community structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.

Suggested Citation

  • Cui, Yaozu & Wang, Xingyuan & Eustace, Justine, 2014. "Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 198-207.
    • Handle: RePEc:eee:phsmap:v:416:y:2014:i:c:p:198-207
    DOI: 10.1016/j.physa.2014.08.050
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    References listed on IDEAS

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    1. Chen, Duanbing & Shang, Mingsheng & Lv, Zehua & Fu, Yan, 2010. "Detecting overlapping communities of weighted networks via a local algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(19), pages 4177-4187.
    2. 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.
    3. Li, W. & Cai, X., 2007. "Empirical analysis of a scale-free railway network in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 693-703.
    4. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517.
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    Cited by:

    1. Nan, Dong-Yang & Yu, Wei & Liu, Xiao & Zhang, Yun-Peng & Dai, Wei-Di, 2018. "A framework of community detection based on individual labels in attribute networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 523-536.
    2. Wang, Tao & Wang, Hongjue & Wang, Xiaoxia, 2015. "A novel cosine distance for detecting communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 21-35.
    3. Wang, Tao & Yin, Liyan & Wang, Xiaoxia, 2018. "A community detection method based on local similarity and degree clustering information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1344-1354.
    4. Zhang, Hong, 2015. "Moderate tolerance promotes tag-mediated cooperation in spatial Prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 52-61.
    5. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.

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