skip to main content
10.1145/2791405.2791482acmotherconferencesArticle/Chapter ViewAbstractPublication PageswciConference Proceedingsconference-collections
research-article

Modeling Evolutionary Group Search Optimization Approach for Community Detection in Social Networks

Authors Info & Claims
Published:10 August 2015Publication History

ABSTRACT

Community structure identification is an important area of research in complex social networks. Uncovering hidden communities in social networks data can help us to visualize and analyze various behavioral and structural phenomenon's occurring in social networks. Detecting communities in networks implies identification of set of clusters that show strong internal cohesion than external cohesion. The problem can be translated into modularity maximization problem which is NP hard. This paper attempts to maximize the modularity of a given network through Mod-GSO, a modification of the Group Search Optimization (GSO) algorithm based on evolutionary animal searching behavior. Mod-GSO modifies the area copying mechanism of the scrounger animals in GSO, by performing a single point crossover instead of real coded GA's crossover to evolve communities. The proposed modification is done to make GSO applicable to Social networks data and evolve scroungers with better community structures and convergence as compared to random evolution of GSO scroungers. Mod-GSO does not require the number of communities to be fixed a-priori and works with a smaller population. Experimental results obtained by Mod-GSO were compared with three well known community detection algorithms named CNM, RB, Multilevel and two evolutionary community detection algorithms named Firefly and GA-Mod using Modularity and NMI metrics on four real world and eleven well known benchmark datasets. The best modularity values of Mod-GSO were observed to be higher than many of the community finding algorithms. The results shows the capability of Mod-GSO for detecting accurate community structures in comparison to other algorithms.

References

  1. Agrawal, R. 2011. Bi-Objective Community Detection in Networks using Genetic Algorithm in Proceedings of 4th International Conference. IC3 2011. (Noida, India, 2011). Contemporary Computing. Springer Berlin Heidelberg, 5--15. DOI=10.1007/978-3-642-22606-9.Google ScholarGoogle Scholar
  2. Amiri, B., Hossain, L., Crawford, J. W. and Wigand, R. T. 2013. Community Detection in Complex Networks: Multiobjective Enhanced Firefly Algorithm. Knowledge based Systems, 46,(July 2013), 1--11. DOI=10.1016/j.knosys.2013.01.004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Blondel, V. D., Guillaume, J. L., Lambiotte, R. and Lefebvre, E. 2008. Fast unfolding of Communities in large networks. Journal of statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI= 10.1088/1742-5468/2008/10/P10008.Google ScholarGoogle ScholarCross RefCross Ref
  4. Clauset, A., Newman, M. E. and Moore, C. 2004. Finding Community structure in a very large network. Physical Review E, 70, 6, 066111. DOI=10.1103/PhysRevE.70.066111.Google ScholarGoogle ScholarCross RefCross Ref
  5. Danon, L., Díaz-Guilera, A., Duch, J. and Arenas, A. 2005. Comparing community structure identification. Journal of stastical Mechanics: Theory and experiment, 2005, 09, P09008, DOI=10.1088/1742-5468/2005/09/P09008.Google ScholarGoogle Scholar
  6. Gach, O. and Hao, J.K. 2012. A memetic algorithm for community detection in complex networks. Parallel Problem Solving from Nature-PPSN XII Lecture Notes in Computer Science, 7492, 2012, 327--336. DOI= 10.1007/978-3-642-32964-7_33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Girvan, M. and Newman, M. E. 2002. Community structure in social and biological networks. Proceedings of National Academy of Sciences, 99, 12, 7821--7826. DOI=10.1073/pnas.122653799.Google ScholarGoogle ScholarCross RefCross Ref
  8. Gonga, M., Maa, L., Zhang, Q. and Jiao, L. 2012. Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Physica A: Statistical Mechanics and its Applications, 391, 15, 4050--4060. DOI=10.1016/j.physa.2012.03.021.Google ScholarGoogle Scholar
  9. Hafez, A. I., Zawbaa, H. M., Hassanien, A. E. and Fahmy, A. A.. 2014. Networks Community detection using Artificial bee colony swarm optimization. in Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 (Czetch Republic,2014), Springer International Publishing, 229--239.DOI=10.1007/978-3-319-08156-4_23.Google ScholarGoogle Scholar
  10. Handle, J. and Knowles, J. 2007. An Evolutionary approach to Multiobjective clustering. IEEE Transactions on Evolutionary Computation, 11, 1, 56--76. DOI= 10.1109/TEVC.2006.877146. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Herrera, F., Lozano, M. and Verdegay, J. L. 1998. Tackling Real Coded Genetic Algorithms Operators and Tools for Behavioural Analysis. Artificial Intelligence Review, 12, 4, 265--319. DOI= 10.1023/A:1006504901164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. He, S., Wu, Q. H. and Saunders, J. R. 2009. Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior. IEEE Transactions on Evolutionary Computation, 13, 5, 973--990. DOI=10.1109/TEVC.2009.2011992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kernighan, B. and Lin, S. 1970. An Efficient Heuristic Procedure for partitioning Graphs. Bell System Technical Journal, 49, 2, 291--307.Google ScholarGoogle ScholarCross RefCross Ref
  14. Lancichinetti, A., Fortunato, S. and Radicchi, F. 2008. Benchmark graphs for testing community detection algorithms. Physical Review E, 78, 4, 046110. DOI= http://dx.doi.org/10.1103/PhysRevE.78.046110.Google ScholarGoogle ScholarCross RefCross Ref
  15. Lusseau, D. and Newman, M. E. 2004. Identifying the role that individual animals play in their social networks. Proceedings of Royal Society of London Series B: Biological Sciences, 271, 6, 477--481. DOI= 10.1098/rsbl.2004.0225.Google ScholarGoogle ScholarCross RefCross Ref
  16. Lusseau, D., Schneider, K., Boisseau, O. J., Haase, P., Slooten, E., and Dawson, S. M. 2003. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behavioural Ecology and Sociobiology, 54, 4, 396--405. DOI=10.1007/s00265-003-0651-y.Google ScholarGoogle ScholarCross RefCross Ref
  17. Newman, M. E. 2004. Fast algorithm for detecting community structure in networks.. Physical Review E, 69, 6, 066133. DOI= http://dx.doi.org/10.1103/PhysRevE.69.066133.Google ScholarGoogle Scholar
  18. Newman, M. E. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Science of the United States of America, 103, 23, 8577--8582. DOI= 10.1073/pnas.0601602103.Google ScholarGoogle ScholarCross RefCross Ref
  19. Park, Y. J. and Song, M. S. 1998. A genetic algorithms for clustering Problems. proceedings of 3rd Annual Conference on Genetic Programming,(United States, July, 1998), Morgan Kauffman, 568--575.Google ScholarGoogle Scholar
  20. Pizzuti, C. 2012. Boosting the Detection of Modular community structure with Genetic algorithms and local search. Proceedings of the 27th Annual ACM Symposium on Applied Computing (Italy, March 2012), ACM DL, 226--231. DOI= 10.1145/2245276.2245321. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Pothen, A., Horst, S. D. and Liou, K.P. 1990. Partitioning sparse matrices with eigenvectors of graphs. SIAM Journal on Matrix Analysis and Applications, 11, 3, 430--452. DOI=10.1137/0611030. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Radicchi, F. et al., 2004. Defining and identifying communities in networks. PNAS, 101, 9, 2658--2663. DOI= 10.1073/pnas.0400054101.Google ScholarGoogle ScholarCross RefCross Ref
  23. Rosavall, M. and Bergstrom, C. 2008. Maps of Random walks on Complex Networks reveal Community Structure. Proceedings of National Academy of Sciences, 105, 4, 1118--1123. DOI= 10.1073/pnas.0706851105.Google ScholarGoogle ScholarCross RefCross Ref
  24. Sadi, S., Etaner-Uyar, S. and Gündüz-Öğüdüc, S. 2009. Community Detection Using Ant Colony Optimization Technique in 15th International Conference on Soft Computing.Google ScholarGoogle Scholar
  25. Shi, C., Yu, P. S., Yan, Z., Huang, Y. and Wang, B. 2014. Comparison and selection of objective functions in multiobjective community detection. Computational Intelligence, 30, 3, 562--582. DOI= 10.1111/coin.12007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Tasgin, M., Herdagdelen, A. and Bingol, H. 2007. Community Detection in Complex Networks using Genetic Algorithm. arXiv:0711.0491 {physics.soc-ph}.Google ScholarGoogle Scholar
  27. Yang, X. S. 2009. Firefly algorithms for multimodal optimisation. Stochastic Algorithms: Foundation and Applications, Springer Berlin, 5792, 2009, 169--178. DOI: 10.1007/978-3-642-04944-6_14. Google ScholarGoogle Scholar
  28. Zachary, W. W. 1977. An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33, 4, 452--473.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Modeling Evolutionary Group Search Optimization Approach for Community Detection in Social Networks

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics
          August 2015
          763 pages
          ISBN:9781450333610
          DOI:10.1145/2791405

          Copyright © 2015 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 10 August 2015

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited

          Acceptance Rates

          WCI '15 Paper Acceptance Rate98of452submissions,22%Overall Acceptance Rate98of452submissions,22%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader