Journal of Intelligent Information Systems

, Volume 43, Issue 1, pp 183–203 | Cite as

Toward seed-insensitive solutions to local community detection

  • Lianhang Ma
  • Hao Huang
  • Qinming He
  • Kevin Chiew
  • Zhenguang Liu
Article

Abstract

Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices’ neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms.

Keywords

Local community detection Similarity Seed-insensitivity 

Notes

Acknowledgements

This work was supported by the National Key Technologies R&D Program of China under Grant No. 2012BAH94F01.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Lianhang Ma
    • 1
  • Hao Huang
    • 2
  • Qinming He
    • 1
  • Kevin Chiew
    • 3
  • Zhenguang Liu
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouPeople’s Republic of China
  2. 2.School of ComputingNational University of SingaporeSingaporeSingapore
  3. 3.Provident Technology Pte. Ltd.SingaporeSingapore

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