Networks Community Detection Using Artificial Bee Colony Swarm Optimization

  • Ahmed Ibrahem Hafez
  • Hossam M. Zawbaa
  • Aboul Ella Hassanien
  • Aly A. Fahmy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


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 work Artificial bee colony (ABC) optimization 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 when used as an objective function within ABC. Experiments on real life networks show the capability of the ABC to successfully find an optimized community structure based on the quality function used.


Networks community detection Community detection Social Networks Artificial bee colony Swarm optimization 


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  1. 1.
    Coleman, J.S.: An introduction to mathematical sociology. Collier-Macmillan, London (1964)Google Scholar
  2. 2.
    Chen, J., Yuan, B.: Detecting functional modules in the yeast protein protein interaction network. Bioinformatics 22(18), 2283–2290 (2006)CrossRefGoogle Scholar
  3. 3.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 7821–7826 (2002)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of USA 101, 2658–2663 (2004)CrossRefGoogle Scholar
  6. 6.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)Google Scholar
  7. 7.
    Wakita, K., Tsurumi, T.: Finding community structure in mega-scale social networks, pp. 1275–1276 (2007)Google Scholar
  8. 8.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physics Rev. E 69, 026113 (2004)Google Scholar
  9. 9.
    Shi, C., Zhong, C., Yan, Z., Cai, Y., Wu, B.: A multi-objective optimization approach for community detection in complex network, Barcelona, Spain, pp. 1–8 (2010)Google Scholar
  10. 10.
    Leskovec, J., Lang, K., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: ACM WWW International Conference on World Wide Web (2010)Google Scholar
  11. 11.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (1997)Google Scholar
  12. 12.
    Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities, pp. 150–160. ACM Press (2000)Google Scholar
  13. 13.
    Lancichinetti, A., Fortunato, S., Kertesz, J.: Detecting the overlapping and hierarchical community structure of complex networks. arXiv:0805.4770v2 (2008)Google Scholar
  14. 14.
    Shi, C., Zhong, C., Yan, Z., Cai, Y., Wu, B.: On selection of objective functions in multi-objective community detection, Glasgow, Scotland, UK, pp. 2301–2304 (2011)Google Scholar
  15. 15.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J. Global Optim. 39, 459–471 (2007)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Mohammadi, A., Ziarati, K., Akbari, R.: A novel bee swarm optimization algorithm for numerical function optimization. Commun. Nonlinear Sci. Numer. Simulat., 3142–3155 (2010)Google Scholar
  17. 17.
    Shi, C., Zhong, C., Yan, Z., Cai, Y., Wu, B.: A new genetic algorithm for community detection. Complex Sciences 5, 1298–1309 (2009)CrossRefGoogle Scholar
  18. 18.
    Pizzuti, C.: GA-net: A genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment 9, 09008 (2005)Google Scholar
  20. 20.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)Google Scholar
  21. 21.
    Lusseau, D.: The emergent properties of dolphin social network. Proceedings of the Royal Society of London. Series B: Biological Sciences 270, S186–S188 (2003)Google Scholar
  22. 22.
    McAuley, J.J., Leskovec, J.: Learning to discover social circles in ego networks, pp. 548–556 (2012)Google Scholar
  23. 23.
    Leskovec, J.: Social Circles in Ego Networks, (Online; accessed 2014)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ahmed Ibrahem Hafez
    • 1
    • 5
  • Hossam M. Zawbaa
    • 3
    • 4
    • 5
  • Aboul Ella Hassanien
    • 2
    • 5
  • Aly A. Fahmy
    • 2
  1. 1.Faculty of Computer and InformationMinia UniversityMinyaEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt
  3. 3.Faculty of Mathematics and Computer ScienceBabes-Bolyai UniversityCluj-NapocaRomania
  4. 4.Faculty of Computers and InformationBeniSuef UniversityBeniSuefEgypt
  5. 5.Scientific Research Group in Egypt (SRGE)CairoEgypt

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