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A multi-label community discovery algorithm based on the community kernel

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Published:25 July 2016Publication History

ABSTRACT

The main research content is to study and design a feasible multi-label community discovery algorithm for the overlapping community network in this article. This community discovery algorithm has low time complexity, and can conveniently realize the overlapping community discovery. Through the experiment to verify the feasibility and effect of this algorithm, compared this multi-label community discovery algorithm with part of the existing community discovery algorithm from two aspects in time and the classification results in the data sets, confirm the feasibility of this community discovery algorithm.

References

  1. Wang Geng. Research to Stable Detecting Overlapping Communities by Label Propagation on Social Networks{D}.Shandong Jianzhu University, 2013.Google ScholarGoogle Scholar
  2. Zhao Baofeng, Zhao Jumin, Li Dengao. A Stable Label Propagation Algorithm for Community Detection{J}. Journal Of Taiyuan University Of Technology, 2013,04:493--495.Google ScholarGoogle Scholar
  3. WuZhihao. Research on Overlapping Community Detection in Complex Networks {D}.Beijing Jiaotong University,2013.Google ScholarGoogle Scholar
  4. Raghavan Usha Nandini, Albert Réka, Kumara Soundar. Near linear time algorithm to detect community structures in large-scale networks{J}. Physical Review E, 2007, 76(3): 036106.Google ScholarGoogle ScholarCross RefCross Ref
  5. Ian X. Y. Leung, Pan Hui, Pietro Liò, Jon Crowcroft. Towards real-time community detection in large networks. Physical Review E, v. 79, (6), June 2009Google ScholarGoogle Scholar
  6. Steve Gregory, Finding overlapping communities in networks by label Propagation, New Journal of Physics, 2010, 12, 103018.Google ScholarGoogle ScholarCross RefCross Ref
  7. Zachary Wayne W. An Information Flow Model for Conflict and Fission in Small Groups{J}. Journal of Anthropological Research. 1977, 33(4): 452--473.Google ScholarGoogle ScholarCross RefCross Ref
  8. Lusseau D., Schneider K., Boisseau O. J., et al. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations{J}. Behavioral Ecology and Sociobiology. 2003, 54(4): 396--405.Google ScholarGoogle ScholarCross RefCross Ref
  9. Lovro Subelj, Marko Bajec, Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction, Phys.Rev.E, 2011, 83, 036103.Google ScholarGoogle ScholarCross RefCross Ref
  10. Bowen Yan, Steve Gregory, Detecting communities in networks by merging cliques, IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS2009, Nov. 20-22, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  11. D. Watts, S. Strogatz. Collective dynamics of 'small-world' networks. Nature, 1998, 393 (6684): 440--442.Google ScholarGoogle ScholarCross RefCross Ref
  12. M. E. J. Newman. Detecting community structure in networks. The European Physical Journal B - Condensed Matter, 2004,38(2): 321--330.Google ScholarGoogle ScholarCross RefCross Ref
  13. ME.J.Newman,Fastalgorithmfordeteetingeommunitystruetureinnetworks,Phys.Rev.E, Jun2004, vol.69,066133.Google ScholarGoogle Scholar
  1. A multi-label community discovery algorithm based on the community kernel

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    • Published in

      cover image ACM Other conferences
      KMO '16: Proceedings of the The 11th International Knowledge Management in Organizations Conference on The changing face of Knowledge Management Impacting Society
      July 2016
      339 pages
      ISBN:9781450340649
      DOI:10.1145/2925995

      Copyright © 2016 ACM

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      Publication History

      • Published: 25 July 2016

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      KMO '16 Paper Acceptance Rate47of96submissions,49%Overall Acceptance Rate47of96submissions,49%
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