Community Detection by an Efficient Ant Colony Approach

  • Lúcio Pereira de Andrade
  • Rogério Pinto Espíndola
  • Nelson Francisco Favilla Ebecken
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


Community detection is an efficient tool to analyze large complex networks offering new insights about their structures and functioning. A community is a significant organizational unity formed by nodes with more connections between them. Ant colony algorithms have been used to detect communities on a fast and efficient way. In this work, changes are performed on an ant colony algorithm for community detection by means of modularity optimization. The changes rely on the way an ant moves and on the adopted stopping criteria. To assess the proposed strategy, benchmark networks are studied and preliminary results indicate that the suggested changes make the original algorithm more robust, reaching higher values of modularity of the detected communities.


complex networks community detection ant colony optimization 


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  1. 1.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2010)CrossRefGoogle Scholar
  2. 2.
    Loscalzo, S., Yu, L.: Social Network Analysis: Tasks and Tools. In: Liu, H., Salerno, J.J., Young, M.J. (eds.) Social Computing, Behavioral Modeling, and Prediction, ch. 17. Springer, New York (2008)Google Scholar
  3. 3.
    Tang, L., Liu, H.: Community Detection and Mining in Social Media, Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool (2010)Google Scholar
  4. 4.
    Fan, T.F., Liau, C.J., Lin, T.Y.: Positional analysis in fuzzy social networks. In: Proceedings of the IEEE International Conference on Granular Computing, San Jose, pp. 423–428 (2007)Google Scholar
  5. 5.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(2), 26113–26127 (2004)CrossRefGoogle Scholar
  6. 6.
    Newman, M.E.J.: Networks: An Introduction. Oxford University Press, New York (2010)Google Scholar
  7. 7.
    Tang, L., Liu, H.: Graph Mining Applications to Social Network Analysis. In: Aggarwal, C.C., Wang, H. (eds.) Managing and Mining Graph Data, Advances in Database Systems, vol. 40. Springer, New York (2010)Google Scholar
  8. 8.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Massachusetts (2004)Google Scholar
  9. 9.
    He, D., Liu, J., Liu, D., et al.: Ant colony optimization for community detection in large-scale complex networks. In: 7th International Conference on Natural Computation, Shangai, China, pp. 1151–1155 (2011)Google Scholar
  10. 10.
    Liu, Y., Liu, L., Luo, J.: Adaptive Ant Colony Clustering Method Applied to Finding Closely Communicating Community. Journal of Networks 7(2), 249–258 (2012)Google Scholar
  11. 11.
    Geetha, M., Nawaz, G.M.K.: Hierarchical Community-Fuzzy Ant Based Dynamic Routing on Large Road Networks. Research Journal of Applied Sciences 8(1), 65–71 (2013)Google Scholar
  12. 12.
    Romdhane, L.B., Chaabani, Y., Zardi, H.: A robust ant colony optimization-based algorithm for community mining in large scale oriented social graphs. Expert Systems with Applications 40(14), 5709–5718 (2013)CrossRefGoogle Scholar
  13. 13.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics 2008(10), P10008 (2008)Google Scholar
  14. 14.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Meyer, J.-A., Wilson, S.W. (eds.) Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, pp. 501–508. MIT Press, Massachusetts (1994)Google Scholar
  16. 16.
    Deneubourg, J.-L., Goss, S., Franks, N., et al.: The dynamics of collective sorting: Robot-like ants and ant-like robots. In: Meyer, J.-A., Wilson, S.W. (eds.) Proceedings of the First International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 356–363. MIT Press, Massachusetts (1991)Google Scholar
  17. 17.
    Urbani, C.B., Boyan, G.S., Blarer, A., et al.: A novel mechanism for jumping in the Indian ant Harpegnathos saltator (Jerdon) (Formicidae, Ponerinae). Experientia 50(1), 63–71 (1994)CrossRefGoogle Scholar
  18. 18.
    Jin, D., Liu, D., Yang, B., et al.: Ant Colony Optimization with a New Random Walk Model for Community Detection in Complex Networks. Advances in Complex Systems 14(5), 795–815 (2011)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lúcio Pereira de Andrade
    • 1
  • Rogério Pinto Espíndola
    • 2
  • Nelson Francisco Favilla Ebecken
    • 3
  1. 1.UFF, Federal Fluminense UniversityRio de JaneiroBrazil
  2. 2.UEZO, State University of West SideRio de JaneiroBrazil
  3. 3.UFRJ, Federal University of Rio de Janeiro (COPPE)Rio de JaneiroBrazil

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