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Detecting Community Structure by Network Vectorization

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Computing and Combinatorics (COCOON 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5092))

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Abstract

With the growing number of available social and biological networks, the problem of detecting network community structure is becoming more and more important which acts as the first step to analyze these data. In this paper, we transform network data so that each node is represented by a vector, our method can handle directed and weighted networks. it also can detect networks which contain communities with different sizes and degree sequences. This paper reveals that network community can be formulated as a cluster problem.

This work is supported by the NNSF (10531070) of China and Science Fund for Creative Research Group. The authors thank Dr. Martin Rosvall in Washington University for giving valuable comments on this paper.

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Author information

Authors and Affiliations

  1. Academy of Mathematics and Systems Science, Chinese Academy of Science,  

    Wei Ren & Guiying Yan

  2. Department of Computing Science, University of Alberta,  

    Guohui Lin

  3. College of Mathematics and Computational Science, China University of Petroleum,  

    Caifeng Du

  4. College of Science, Shandong University of Science and Technology,  

    Xiaofeng Han

Authors
  1. Wei Ren
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  2. Guiying Yan
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  3. Guohui Lin
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  4. Caifeng Du
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  5. Xiaofeng Han
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Editor information

Xiaodong Hu Jie Wang

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Ren, W., Yan, G., Lin, G., Du, C., Han, X. (2008). Detecting Community Structure by Network Vectorization. In: Hu, X., Wang, J. (eds) Computing and Combinatorics. COCOON 2008. Lecture Notes in Computer Science, vol 5092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69733-6_25

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  • DOI: https://doi.org/10.1007/978-3-540-69733-6_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69732-9

  • Online ISBN: 978-3-540-69733-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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