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A Vertex Similarity Probability Model for Finding Network Community Structure

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Advances in Knowledge Discovery and Data Mining (PAKDD 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7301))

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Abstract

Most methods for finding community structure are based on the prior knowledge of network structure type. These methods grouped the communities only when known network is unipartite or bipartite. This paper presents a vertex similarity probability (VSP) model which can find community structure without priori knowledge of network structure type. Vertex similarity, which assumes that, for any type of network structures, vertices in the same community have similar properties. In the VSP model, “Common neighbor index” is used to measure the vertex similarity probability, as it has been proved to be an effective index for vertex similarity. We apply the algorithm to real-world network data. The results show that the VSP model is uniform for both unipartite networks and bipartite networks, and it is able to find the community structure successfully without the use of the network structure type.

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Author information

Authors and Affiliations

  1. Beijing Key Lab of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Kan Li & Yin Pang

  2. Beijing Institute of Tracking and Telecommunication Technology, Beijing, 100094, China

    Yin Pang

Authors
  1. Kan Li
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  2. Yin Pang
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Michigan State University, 428 S. Shaw Lane, 48824-1226, East Lansing, MI, USA

    Pang-Ning Tan

  2. School of Information Technologies, University of Sydney, 1 Cleveland St., 2006, Sydney, NSW, Australia

    Sanjay Chawla

  3. Faculty of Computing and Informatics, Jalan Multimedia, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Chin Kuan Ho

  4. Department of Computing and Information Systems, The University of Melbourne, 111 Barry Street, 3053, Melbourne, VIC, Australia

    James Bailey

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, K., Pang, Y. (2012). A Vertex Similarity Probability Model for Finding Network Community Structure. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_38

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  • DOI: https://doi.org/10.1007/978-3-642-30217-6_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30216-9

  • Online ISBN: 978-3-642-30217-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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