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Complex Networks pp 208–216Cite as

On Community Detection in Very Large Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 116))

Abstract

Community detection or graph clustering is an important problem in the analysis of computer networks, social networks, biological networks and many other natural and artificial networks. These networks are in general very large and, thus, finding hidden structures and functional modules is a very hard task. In this paper we propose new data structures and a new implementation of a well known agglomerative greedy algorithm to find community structure in large networks, the CNM algorithm. The experimental results show that the improved data structures speedup the method by a large factor, for large networks, making it competitive with other state of the art algorithms.

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Author information

Authors and Affiliations

  1. INESC-ID / CSE Dept, IST, Tech Univ of Lisbon, Rua Alves Redol 9, 1000-029, Lisboa, Portugal

    Alexandre P. Francisco & Arlindo L. Oliveira

Authors
  1. Alexandre P. Francisco
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  2. Arlindo L. Oliveira
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Editor information

Editors and Affiliations

  1. Institute of Physics at Sao Carlos, University of Sao Paulo, P.O.Box 369, 13560-970, Sao Carlos, Sao Paulo, Brazil

    Luciano da F. Costa

  2. Universidade Federal do Rio de Janeiro (UFRJ), COPPE/Federal University of Rio de Janeiro, Box 68506, 21941-972, Rio de Janeiro, RJ, Brazil

    Alexandre Evsukoff

  3. Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), Viale A. Doria, 6, 95125, Catania, Italy

    Giuseppe Mangioni

  4. Florida Institute of Technology, Computer Sciences, 150 W. University Blvd, 32901, Melbourne, FL, USA

    Ronaldo Menezes

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Francisco, A.P., Oliveira, A.L. (2011). On Community Detection in Very Large Networks. In: da F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds) Complex Networks. Communications in Computer and Information Science, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25501-4_21

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  • DOI: https://doi.org/10.1007/978-3-642-25501-4_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25500-7

  • Online ISBN: 978-3-642-25501-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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