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Mining overlapping and hierarchical communities in complex networks

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  • Zhang, Zhiwei
  • Wang, Zhenyu

Abstract

Community detection in the social networks is one of the most important tasks of social computing. Highly relevant researches indicate that the social network generally contains both an overlapping and hierarchical structure. This paper introduces an efficient and functional community detection algorithm MOHCC, which can concurrently discover overlapping and hierarchical organization in complex networks. This algorithm first extracts all maximal cliques from the original complex network. Merges all extracted maximal cliques into a dendrogram by using the aggregative framework presented in MOHCC. Finally, it cuts through the dendrogram and obtains a network partition with maximum extended partition density. Experimental results utilizing computer-generated artificial networks and real-world social benchmark networks give satisfactory correspondence.

Suggested Citation

  • Zhang, Zhiwei & Wang, Zhenyu, 2015. "Mining overlapping and hierarchical communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 25-33.
    • Handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:25-33
    DOI: 10.1016/j.physa.2014.11.023
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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. Cui, Yaozu & Wang, Xingyuan & Li, Junqiu, 2014. "Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 85-91.
    3. Mu, Caihong & Liu, Yong & Liu, Yi & Wu, Jianshe & Jiao, Licheng, 2014. "Two-stage algorithm using influence coefficient for detecting the hierarchical, non-overlapping and overlapping community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 47-61.
    4. Badie, Reza & Aleahmad, Abolfazl & Asadpour, Masoud & Rahgozar, Maseud, 2013. "An efficient agent-based algorithm for overlapping community detection using nodes’ closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5231-5247.
    5. 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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    Cited by:

    1. Chen, Zigang & Li, Lixiang & Peng, Haipeng & Liu, Yuhong & Yang, Yixian, 2018. "Attributed community mining using joint general non-negative matrix factorization with graph Laplacian," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 324-335.
    2. Huang, Zhenhua & Wu, Junxian & Zhu, Wentao & Wang, Zhenyu & Mehrotra, Sharad & Zhao, Yangyang, 2021. "Visualizing complex networks by leveraging community structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Zhou, Kuang & Martin, Arnaud & Pan, Quan, 2015. "A similarity-based community detection method with multiple prototype representation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 519-531.
    4. Zhang, Zhiwei & Wang, Zhenyu, 2017. "The data-driven null models for information dissemination tree in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 394-411.

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