Advertisement

Detecting Overlapping Communities in Social Networks with Voronoi and Tolerance Rough Sets

  • Kushagra Trivedi
  • Sheela Ramanna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

In this work, we propose a novel method based on Voronoi diagrams and tolerance rough set method (TRSM) to detect overlapping communities. In the proposed Voronoi TRSM approach, a social network is represented as a graph. A Voronoi diagram is a partitioning of a plane into regions based on closeness to points in a specific set of sites (seeds). These seeds are used as a core for determining tolerance classes. The upper approximation operator from TRSM is used to obtain overlapping nodes. We have experimented with three well-known real networks and compared with Fuzzy-Rough and a Matrix Factorization-based approach. The results with proposed Voronoi TRSM approach are promising in terms of the extended modularity measure and the dense communities measure.

Keywords

Community detection Density-based clustering Social networks Soft computing Tolerance rough sets Voronoi diagram 

References

  1. 1.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  2. 2.
    Campigotto, R., Céspedes, P.C., Guillaume, J.L.: A generalized and adaptive method for community detection. ArXiv preprint arXiv:1406.2518 (2014)
  3. 3.
    Cao, X., Wang, X., Jin, D., Cao, Y., He, D.: Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization. Sci. Rep. 3, 2993 (2013)CrossRefGoogle Scholar
  4. 4.
    Chen, Q., Wu, T.T., Fang, M.: Detecting local community structures in complex networks based on local degree central nodes. Phys. A Stat. Mech. Appl. 392(3), 529–537 (2013)CrossRefGoogle Scholar
  5. 5.
    Deritei, D., Lázár, Z.I., Papp, I., Járai-Szabó, F., Sumi, R., Varga, L., Regan, E.R., Ercsey-Ravasz, M.: Community detection by graph Voronoi diagrams. New J. Phys. 16(6), 063007 (2014)CrossRefGoogle Scholar
  6. 6.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kundu, S., Pal, S.K.: Fuzzy-rough community in social networks. Pattern Recogn. Lett. 67, 145–152 (2015)CrossRefGoogle Scholar
  8. 8.
    Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033015 (2009)CrossRefGoogle Scholar
  9. 9.
    Lusseau, D.: The emergent properties of a dolphin social network. Proc. Roy. Soc. Lond. B Biol. Sci. 270(Suppl 2), S186–S188 (2003)CrossRefGoogle Scholar
  10. 10.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks (2004). http://arxiv.org/pdf/cond-mat/0308217v1:PDF
  11. 11.
    Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)CrossRefGoogle Scholar
  12. 12.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)CrossRefGoogle Scholar
  13. 13.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  14. 14.
    Peters, J., Ramanna, S.: Proximal three-way decisions: theory and applications in social networks. Knowl. Based Syst. Elsevier 91, 4–15 (2016)CrossRefGoogle Scholar
  15. 15.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Nat. Acad. Sci. USA 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  16. 16.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Nat. Acad. Sci. 105(4), 1118–1123 (2008)CrossRefGoogle Scholar
  17. 17.
    Shen, H., Cheng, X., Cai, K., Hu, M.B.: Detect overlapping and hierarchical community structure in networks. Phys. A Stat. Mech. Appl. 388(8), 1706–1712 (2009)CrossRefGoogle Scholar
  18. 18.
    Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27(2, 3), 245–253 (1996)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Whang, J.J., Gleich, D.F., Dhillon, I.S.: Overlapping community detection using seed set expansion. In: Proceedings of ACM International Conference onIinformation & Knowledge Management, pp. 2099–2108 (2013)Google Scholar
  20. 20.
    Wu, H., Gao, L., Dong, J., Yang, X.: Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks. PLoS ONE 9(3), e91856 (2014)CrossRefGoogle Scholar
  21. 21.
    Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 43:1–43:35 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied Computer ScienceUniversity of WinnipegWinnipegCanada

Personalised recommendations