Skip to main content
SpringerLink
Log in

GA-Net: A Genetic Algorithm for Community Detection in Social Networks

  • Conference paper
Book cover Parallel Problem Solving from Nature – PPSN X (PPSN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5199))

Included in the following conference series:

Abstract

The problem of community structure detection in complex networks has been intensively investigated in recent years. In this paper we propose a genetic based approach to discover communities in social networks. The algorithm optimizes a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups. The method is efficient because the variation operators are modified to take into consideration only the actual correlations among the nodes, thus sensibly reducing the research space of possible solutions. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Angiulli, F., Cesario, E., Pizzuti, C.: A greedy search approach to co-clustering sparse binary matrices. In: Proceedings of the 18th International Conference on Tools with Artificial Intelligence (ICTAI 2006), pp. 363–370 (2006)

    Google Scholar 

  2. Arenas, A., Diaz-Guilera, A.: Synchronization and modularity in complex networks. European Physical Journal ST 143, 19–25 (2007)

    Article  Google Scholar 

  3. Borgatti, S.P.: Netdraw 1.0: Network visualization software. Harvard: Analytic technologies (2002)

    Google Scholar 

  4. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E 70, 066111 (2004)

    Article  Google Scholar 

  5. Danon, L., Díaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics, P09008 (2005)

    Google Scholar 

  6. Dubes, R.C., Jain, A.K.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

  7. Firat, A., Chatterjee, S., Yilmaz, M.: Genetic clustering of social networks using random walk. Computational Statistics and Data Analysis 51, 6285–6294 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  8. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. National. Academy of Science. USA 99, 7821–7826 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  9. Handle, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE transactions on Evolutionary Computation 11(1), 56–76 (2007)

    Article  Google Scholar 

  10. Hopcroft, J.E., Khan, O., Kulis, B., Selman, B.: Natural communities in large linked networks. In: Proc. International Conference on Knowledge Discovery and Data Mining (KDD 2003), pp. 541–546 (2003)

    Google Scholar 

  11. Lozano, S., Duch, J., Arenas, A.: Analysis of large social datasets by community detection. European Physical Journal ST 143, 257–259 (2007)

    Article  Google Scholar 

  12. Lusseau, D.: The emergent properties of dolphin social network. Biology Letters, Proc. R. Soc. London B (suppl.) (2003)

    Google Scholar 

  13. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics 1(1), 24–45 (2004)

    Article  Google Scholar 

  14. Makate, N., Miki, M., Hiroyasu, T., Senda, T.: Multiobjective clustering with automatic k-determination fot large-scale dara. In: Proc. of the Int. Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 861–868 (2007)

    Google Scholar 

  15. Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  16. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69, 066133 (2004)

    Article  Google Scholar 

  17. Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577–8582 (2006)

    Article  Google Scholar 

  18. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113 (2004)

    Article  Google Scholar 

  19. Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems. In: Proc. of 3rd Annual Conference on Genetic Algorithms, pp. 2–9 (1989)

    Google Scholar 

  20. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natl. Acad.Sci. USA (PNAS 2004) 101(9), 2658–2663 (2004)

    Article  Google Scholar 

  21. Tasgin, M., Bingol, A.: Communities detection in complex networks using genetic algorithms. In: Proc. of the European Conference on Complex Systems (ECSS 2006) (2006)

    Google Scholar 

  22. Tasgin, M., Herdagdelen, A., Bingol, A.: Communities detection in complex networks using genetic algorithms. oai:arXiv.org:0711.0491v1 [physics.soc-ph] (2007)

    Google Scholar 

  23. Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICAR-CNR, Via P. Bucci 41C, 87036, Rende (CS), Italy

    Clara Pizzuti

Authors
  1. Clara Pizzuti
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

  2. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Thomas Jansen  & Nicola Beume  & 

  3. Department of Computing and Electronic Systems, University of Essex, CO4 3SQ, Colchester, Essex, UK

    Simon Lucas

  4. Dipartimento di Ingegneria Meccanica, Università degli Studi di Trieste, 34127, Trieste, Italy

    Carlo Poloni

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pizzuti, C. (2008). GA-Net: A Genetic Algorithm for Community Detection in Social Networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_107

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87700-4_107

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87699-1

  • Online ISBN: 978-3-540-87700-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation