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FVBM: A Filter-Verification-Based Method for Finding Top-k Closeness Centrality on Dynamic Social Networks

  • Yiyong Lin
  • Jinbo Zhang
  • Yuanxiang Ying
  • Shenda Hong
  • Hongyan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)

Abstract

Closeness centrality is often used to identify the top-k most prominent nodes in a network. Real networks, however, are rapidly evolving all the time, which results in the previous methods hard to adapt. A more scalable method that can immediately react to the dynamic network is demanding. In this paper, we endeavour to propose a filter and verification framework to handle such new trends in the large-scale network. We adopt several pruning methods to generate a much smaller candidate set so that bring down the number of necessary time-consuming calculations. Then we do verification on the subset; which is a much time efficient manner. To further speed up the filter procedure, we incremental update the influenced part of the data structure. Extensive experiments using real networks demonstrate its high scalability and efficiency.

Keywords

Closeness centrality Filter-Verification Dynamic social network 

Notes

Acknowledgments

This work was supported by Natural Science Foundation of China (No. 61170003).

References

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    Yen, C.C., Yeh, M.Y., Chen, M.S.: An efficient approach to updating closeness centrality and average path length in dynamic networks. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 867–876. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yiyong Lin
    • 1
    • 2
  • Jinbo Zhang
    • 1
    • 2
  • Yuanxiang Ying
    • 1
    • 2
  • Shenda Hong
    • 1
    • 2
  • Hongyan Li
    • 1
    • 2
  1. 1.Key Laboratory of Machine PerceptionPeking University, Ministry of EducationBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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