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Community Detection Algorithm Based on Artificial Fish Swarm Optimization

  • Conference paper
Book cover Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this paper Artificial Fish Swarm optimization (AFSO) has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures and other well-known methods. Experiments on real life networks show the capability of the AFSO to successfully find an optimized community structure based on the quality function used.

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Eslam Ali Hassan, Aboul Ella Hassanien & Aly A. Fahmy

  2. Faculty of Computer and Information, Minia University, Minya, Egypt

    Ahmed Ibrahem Hafez

  3. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Eslam Ali Hassan, Ahmed Ibrahem Hafez & Aboul Ella Hassanien

Authors
  1. Eslam Ali Hassan
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  2. Ahmed Ibrahem Hafez
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  3. Aboul Ella Hassanien
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  4. Aly A. Fahmy
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Corresponding author

Correspondence to Eslam Ali Hassan .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

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Hassan, E.A., Hafez, A.I., Hassanien, A.E., Fahmy, A.A. (2015). Community Detection Algorithm Based on Artificial Fish Swarm Optimization. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_44

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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