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Local Community Detection and Visualization: Experiment Based on Student Data

  • Miloš Kudělka
  • Pavla Dráždilová
  • Eliška Ochodková
  • Kateřina Slaninová
  • Zdeněk Horák
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 179)

Abstract

This paper is focused on the detection of communities in social networks. We propose and describe a novel method for detecting local communities. We have used this method in an experiment on student social networks in order to prove our hypothesis about the nature of student communities. The results of the experiment rationalized our hypothesis and confirmed the effectiveness of the described method of local community detection.

Notes

Acknowledgments

This work was partially supported by SGS, VSB-Technical University of Ostrava, Czech Republic, under the grants No. SP2011/172.

References

  1. 1.
    Bagrow, J.P.: Evaluating local community methods in networks. J. Stat. Mech. 2008(5), P05001 (2008)CrossRefGoogle Scholar
  2. 2.
    Bagrow, J.P., Bollt, E.M.: A local method for detecting communities. Phys. Rev. E 72, 046108 (2005)CrossRefGoogle Scholar
  3. 3.
    Chen, J., Zaïane, O.R., Goebel, R.: Detecting communities in large networks by iterative local expansion. In: CASoN IEEE Computer Society, pp. 105–112. (2009)Google Scholar
  4. 4.
    Chen, J., Zaïane, O.R., Goebel, R.: Local community identification in social networks. In: ASONAM, IEEE Computer Society, pp. 237–242. (2009)Google Scholar
  5. 5.
    Chen, J., Zaiïane, O.R., Goebel, R.: Detecting communities in social networks using max-min modularity. In: SDM, SIAM, pp. 978–989. (2009)Google Scholar
  6. 6.
    Chen, Q., Wu, T.T.: A method for local community detection by finding maximal-degree nodes. In: ICMLC, pp. 8–13 (2010)Google Scholar
  7. 7.
    Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026132 (2005)CrossRefGoogle Scholar
  8. 8.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)CrossRefGoogle Scholar
  9. 9.
    Fiedler, M.: Algebraic connectivity of graphs. Czech. Math. J. 23, 298–305 (1973)MathSciNetGoogle Scholar
  10. 10.
    Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–308 (1970)MATHGoogle Scholar
  11. 11.
    Horák, Z., Snášel, V., Abraham, A., Kudělka, M.: Weighted co-authorship network based on forgetting. Future Inf. Technol. 185, 72–79 (2011)CrossRefGoogle Scholar
  12. 12.
    Luo, F., Wang, J., Promislow, E.: Exploring local community structures in large networks. In: IEEE/WIC/ACM International Conference on WI, pp. 233–239. (2006)Google Scholar
  13. 13.
    Newman, M.: Networks: An Introduction. Oxford University Press, Inc., New York (2010)MATHGoogle Scholar
  14. 14.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004). PT: J; PN: Part 2; PG: 15Google Scholar
  15. 15.
    Pothen, A., Simon, H.D., Liou, K.P.: Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11, 430–452 (1990)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Schaeffer, S.E.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wakita, K., Tsurumi, T.: Finding community structure in mega-scale social networks: [extended abstract]. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pp. 1275–1276. ACM, New York, NY, USA (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Miloš Kudělka
    • 1
  • Pavla Dráždilová
    • 1
  • Eliška Ochodková
    • 1
  • Kateřina Slaninová
    • 1
  • Zdeněk Horák
    • 1
  1. 1.Department of Computer ScienceFEECS, VŠB—Technical University of OstravaOstravaCzech Republic

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