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Community mining with new node similarity by incorporating both global and local topological knowledge in a constrained random walk

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  • Jiao, Qing-Ju
  • Huang, Yan
  • Shen, Hong-Bin

Abstract

Detection of community is a crucial step to understand the structure and dynamics of complex networks. Most of conventional community detection methods focus on optimizing a certain objective function or on clustering nodes based on their similarities, which leads to a phenomenon that they have preference for specific types of networks but are not general. Using constrained random walk, we exploit global and local topology structures of network to propose a modified transition matrix and further to define a new similarity metric (named ISIM) between two nodes. In contrast to the existing similarities, ISIM does not work directly on the observed data, but in a convergent stable space. This feature makes ISIM robust to the observed noisy data in real-world networks. ISIM not only measures node’s distance, but also captures node’s topology structure in network. Experiments on synthetic and real-world networks demonstrate that ISIM can be successfully applied to community detection in broader types of networks and outperforms other community detection methods.

Suggested Citation

  • Jiao, Qing-Ju & Huang, Yan & Shen, Hong-Bin, 2015. "Community mining with new node similarity by incorporating both global and local topological knowledge in a constrained random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 363-371.
  • Handle: RePEc:eee:phsmap:v:424:y:2015:i:c:p:363-371
    DOI: 10.1016/j.physa.2015.01.022
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    References listed on IDEAS

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    1. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    2. Capocci, A. & Servedio, V.D.P. & Caldarelli, G. & Colaiori, F., 2005. "Detecting communities in large networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(2), pages 669-676.
    3. 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.
    4. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
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    Cited by:

    1. Li, Wei & Huang, Ce & Wang, Miao & Chen, Xi, 2017. "Stepping community detection algorithm based on label propagation and similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 145-155.

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