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An Extended Validity Index for Identifying Community Structure in Networks

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Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6064))

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Abstract

To find the best partition of a large and complex network into a small number of communities has been addressed in many different ways. In this paper, a new validity index for network partition is proposed, which is motivated by the construction of Xie-Beni index in Euclidean space. The simulated annealing strategy is used to minimize this extended validity index, associating with a dissimilarity-index-based k-means iterative procedure, under the framework of a random walker Markovian dynamics on the network. The proposed algorithm(SAEVI) can efficiently and automatically identify the community structure of the network and determine an appropriate number of communities without any prior knowledge about the community structure during the cooling process. The computational results on several artificial and real-world networks confirm the capability of the algorithm.

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Author information

Authors and Affiliations

  1. LMAM and School of Mathematical Sciences, Peking University, Beijing, 100871, P.R. China

    Jian Liu

Authors
  1. Jian Liu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang  & Bao-Liang Lu  & 

  2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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Liu, J. (2010). An Extended Validity Index for Identifying Community Structure in Networks. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_33

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  • DOI: https://doi.org/10.1007/978-3-642-13318-3_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13317-6

  • Online ISBN: 978-3-642-13318-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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