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Noise-tolerance community detection and evolution in dynamic social networks

Author

Listed:
  • Li Wang

    (College of Computer Science and Technology, Taiyuan University of Technology)

  • Jiang Wang

    (College of Computer Science and Technology, Taiyuan University of Technology)

  • Yuanjun Bi

    (University of Texas at Dallas)

  • Weili Wu

    (University of Texas at Dallas)

  • Wen Xu

    (University of Texas at Dallas)

  • Biao Lian

    (College of Computer Science and Technology, Taiyuan University of Technology)

Abstract

Dynamic complex social network is always mixed with noisy data and abnormal events always influence the network. It is important to track dynamic community evolution and discover the abnormal events for understanding real world. In this paper, we propose a novel algorithm Noise-Tolerance Community Detection (NTCD) to discover dynamic community structure that is based on historical information and current information. An updated algorithm is introduced to help find the community structure snapshot at each time step. One evaluation method based on structure and connection degree is proposed to measure the community similarity. Based on this evaluation, the latent community evolution can be tracked and abnormal events can be gotten. Experiments on different real datasets show that NTCD not only eliminates the influence of noisy data but also discovers the real community structure and abnormal events.

Suggested Citation

  • Li Wang & Jiang Wang & Yuanjun Bi & Weili Wu & Wen Xu & Biao Lian, 2014. "Noise-tolerance community detection and evolution in dynamic social networks," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 600-612, October.
    • Handle: RePEc:spr:jcomop:v:28:y:2014:i:3:d:10.1007_s10878-014-9719-z
    DOI: 10.1007/s10878-014-9719-z
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    References listed on IDEAS

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    1. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    2. 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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