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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2015: Hybrid Artificial Intelligent Systems pp 188–199Cite as

A Discrete Bat Algorithm for the Community Detection Problem

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9121))

Abstract

Community detection in networks has raised an important research topic in recent years. The problem of detecting communities can be modeled as an optimization problem where a quality objective function that captures the intuition of a community as a set of nodes with better internal connectivity than external connectivity is selected to be optimized. In this work the Bat algorithmwas used as an optimization algorithm to solve the community detection problem. Bat algorithm is a new Nature-inspired metaheuristic algorithm that proved its good performance in a variety of applications. However, the algorithm performance is influenced directly by the quality function used in the optimization process. Experiments on real life networks show the ability of the Bat algorithm to successfully discover an optimized community structure based on the quality function used and also demonstrate the limitations of the BA when applied to the community detection problem.

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References

  1. Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  2. Ali, A.S, Hussien, A.S, Tolba, M.F, Youssef, A.H.: Visualization of large time-varying vector data. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 4, pp. 210–215. IEEE (2010)

    Google Scholar 

  3. Masdarolomoor, Z., Azmi, R., Aliakbary, S., Riahi, N.: Finding community structure in complex networks using parallel approach. In: 2011 IFIP 9th International Conference on Embedded and Ubiquitous Computing (EUC), pp. 474–479, October 2011

    Google Scholar 

  4. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99, 7821–7826 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  5. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)

    Article  Google Scholar 

  6. Shi, C., Zhong, C., Yan, Z., Cai, Y., Wu, B.: A multi-objective approach for community detection in complex network. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)

    Google Scholar 

  7. Leskovec, J., Lang, K.J, Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, pp. 631–640. ACM (2010)

    Google Scholar 

  8. Shi, C., Yu, P.S., Cai, Y., Yan, Z., Wu, B.: On selection of objective functions in multi-objective community detection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2301–2304. ACM (2011)

    Google Scholar 

  9. Hafez, A.I., Al-Shammari, E.M., ella Hassanien, A., Fahmy, A.A.: Genetic algorithms for multi-objective community detection in complex networks. In: Pedrycz, W., Chen, S.-M. (eds.) Social Networks: A Framework of Computational Intelligence. Studies in Computational Intelligence, pp. 145–171. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  10. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  11. Yang, X.-S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput. 5(3), 141–149 (2013)

    Article  Google Scholar 

  12. Shi, C., Wang, Y., Wu, B., Zhong, C.: A new genetic algorithm for community detection. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 5, pp. 1298–1309. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  13. 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)

    Chapter  Google Scholar 

  14. Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech Theor. Exp. 9, 9008 (2005)

    Article  Google Scholar 

  15. Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)

    Google Scholar 

  16. Lusseau, D.: The emergent properties of dolphin social network. Proc. Roy. Soc. Lond. Ser. B Biol. Sci. 270, S186–S188 (2003)

    Article  Google Scholar 

  17. McAuley, J.J., Leskovec, J.: Learning to discover social circles in ego networks, pp. 548–556 (2012)

    Google Scholar 

  18. Hafez, A.I., Hassanien, A.E., Fahmy, A.A.: Testing community detection algorithms: a closer look at datasets. In: Panda, M., Dehuri, S., Wang, G.-N. (eds.) Social Networking. ISRL, vol. 65, pp. 87–102. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  19. Hassan, E.A., Hafez, A.I., Hassanien, A.E., Fahmy, A.A.: Community detection algorithm based on artificial fish swarm optimization. In: Filev, D., et al. (eds.) Intelligent Systems 2014. AISC, pp. 509–521. Springer International Publishing, Heidelberg (2015)

    Chapter  Google Scholar 

  20. Rosvall, M., Axelsson, D., Bergstrom, C.T.: The map equation. Eur. Phy. J. Spec. Top. 178(1), 13–23 (2009)

    Article  Google Scholar 

  21. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)

    Article  Google Scholar 

  22. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)

    Article  Google Scholar 

  23. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), 10008 (2008)

    Article  Google Scholar 

  24. Pons, P., Latapy, M.: Computing communities in large networks using random walks (long version 12 (2005). ArXiv Physics e-prints

    Google Scholar 

  25. Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

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

    Eslam A. 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), Giza, Egypt

    Eslam A. Hassan, Ahmed Ibrahem Hafez & Aboul Ella Hassanien

Authors
  1. Eslam A. 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 Aboul Ella Hassanien .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Enrique Onieva

  2. University of Deusto, Bilbao, Spain

    Igor Santos

  3. University of Deusto, Bilbao, Spain

    Eneko Osaba

  4. Universidad de Salamanca, Salamanca, Spain

    Héctor Quintián

  5. Universidad de Salamanca, Salamanca, Spain

    Emilio Corchado

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© 2015 Springer International Publishing Switzerland

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Hassan, E.A., Hafez, A.I., Hassanien, A.E., Fahmy, A.A. (2015). A Discrete Bat Algorithm for the Community Detection Problem. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-19644-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19643-5

  • Online ISBN: 978-3-319-19644-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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