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On the Community Identification in Weighted Time-Varying Networks

  • Youcef AbdelsadekEmail author
  • Kamel Chelghoum
  • Francine Herrmann
  • Imed Kacem
  • Benoît Otjacques
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10103)

Abstract

The community detection play an important role in understanding the information underlying to the graph structure, especially, when the graph structure or the weights between the linked entities change over time. In this paper, we propose an algorithm for the community identification in weighted and dynamic graphs, called Dyci. The latter takes advantage from the previously detected communities. Several changes’ scenarios are considered like, node/edge addition, node/edge removing and edge weight update. The main idea of Dyci is to track whether a connected component of the weighted graph becomes weak over time, in order to merge it with the “dominant” neighbour community. In order to assess the quality of the returned community structure, we conduct a comparison with a genetic algorithm on real-world data of the ANR-Info-RSN project. The conducted comparison shows that Dyci provides a good trade-off between efficiency and consumed time.

Keywords

Dynamic networks Community detection Genetic algorithm Weighted graphs Twitter’s networks 

Notes

Acknowledgments

This research has been supported by the Agence Nationale de la Recherche (ANR, France) during the Info-RSN Project (ANR-13-SOIN-0008).

References

  1. 1.
    Harary, F., Gupta, G.: Dynamic graph models. Math. Comput. Model. 25(7), 79–87 (1997). http://dx.doi.org/10.1016/S0895-7177(97)00050-2 MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Diehl, S., Görg, C.: Graphs, they are changing. In: Goodrich, M.T., Kobourov, S.G. (eds.) GD 2002. LNCS, vol. 2528, pp. 23–31. Springer, Heidelberg (2002). doi: 10.1007/3-540-36151-0_3 CrossRefGoogle Scholar
  3. 3.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 26113 (2004)CrossRefGoogle Scholar
  4. 4.
    Blondel, V., Guillaume, J., Lambiotte, R., Mech, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 10, 10008 (2008)CrossRefGoogle Scholar
  5. 5.
    Tantipathananandh, C., Berger-Wolf, T., Kempe, D.: A framework for community identification in dynamic social networks. In: KDD 2007: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 717–726. NY, USA (2007). http://portal.acm.org/citation.cfm?doid=1281192.1281269
  6. 6.
    Takaffoli, M., Sangi, F., Fagnan, J., Zane, O.R.: Community evolution mining in dynamic social networks. Procedia Soc. Behav. Sci. 22, 49–58 (2011). dynamics of Social Networks 7th Conference on Applications of Social Network Analysis-ASNA2010. http://www.sciencedirect.com/science/article/pii/S1877042811013784 CrossRefGoogle Scholar
  7. 7.
    Bansal, S., Bhowmick, S., Paymal, P.: Fast community detection for dynamic complex networks. In: F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds.) CompleNet 2010. CCIS, vol. 116, pp. 196–207. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25501-4_20 CrossRefGoogle Scholar
  8. 8.
    Aktunc, R., Toroslu, I.H., Ozer, M., Davulcu, H.: A dynamic modularity based community detection algorithm for large-scale networks: Dslm. In: Pei, J., Silvestri, F., Tang, J. (eds.) ASONAM, pp. 1177–1183. ACM (2015). http://dblp.uni-trier.de/db/conf/asunam/asonam2015.html#AktuncTOD15
  9. 9.
    Nguyen, N.P., Dinh, T.N., Xuan, Y., Thai, M.T.: Adaptive algorithms for detecting community structure in dynamic social networks. In: INFOCOM, pp. 2282–2290. IEEE (2011). http://dblp.uni-trier.de/db/conf/infocom/infocom2011.html#NguyenDXT11
  10. 10.
    Alvari, H., Hajibagheri, A., Sukthankar, G.R.: Community detection in dynamic social networks: A game-theoretic approach. In: Wu, X., Ester, M., Xu, G. (eds.) ASONAM, pp. 101–107. IEEE Computer Society (2014). http://dblp.uni-trier.de/db/conf/asunam/asonam2014.html#AlvariHS14
  11. 11.
    Tantipathananandh, C., Berger-Wolf, T.Y.: Constant-factor approximation algorithms for identifying dynamic communities. In: Iv, J.F.E., Fogelman-Souli, F., Flach, P.A., Zaki, M. (eds.) KDD, pp. 827–836. ACM (2009). http://dblp.uni-trier.de/db/conf/kdd/kdd2009.html#TantipathananandhB09
  12. 12.
    Hopcroft, J., Khan, O., Kulis, B., Selman, B.: Tracking evolving communities in large linked networks. In: PNAS (2004)Google Scholar
  13. 13.
    Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities in dynamic social networks. In: Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, pp. 176–183. ASONAM 2010, (2010). http://dx.doi.org/10.1109/ASONAM.2010.17
  14. 14.
    Xie, J., Chen, M., Szymanski, B.K.: Labelrankt: Incremental community detection in dynamic networks via label propagation. CoRR abs/1305.2006 (2013). http://dblp.uni-trier.de/db/journals/corr/corr1305.html#abs-1305-2006
  15. 15.
    Abdelsadek, Y., Chelghoum, K., Herrmann, F., Kacem, I., Otjacques, B.: Community detection algorithm based on weighted maximum triangle packing. In: Proceedings of International Conference on Computer and Industrial Engineering CIE45 (2015)Google Scholar
  16. 16.
    Park, Y., Song, M.: A genetic algorithm for clustering problems. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 568–575. Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA, 22–25 July 1998Google Scholar
  17. 17.
    Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-87700-4_107 CrossRefGoogle Scholar
  18. 18.
    Jin, D., He, D., Liu, D., Baquero, C.: Genetic algorithm with local search for community mining in complex networks. In: ICTAI (1), pp. 105–112. IEEE Computer Society (2010). http://dblp.uni-trier.de/db/conf/ictai/ictai2010-1.html#JinHLB10
  19. 19.
    Newman, M.: Modularity and community structure in networks. Proc. Nat. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  20. 20.
    Abdelsadek, Y., Chelghoum, K., Herrmann, F., Kacem, I., Otjacques, B.: Visual interactive approach for mining twitter’s networks. In: Tan, Y., Shi, Y. (eds.) Data Mining and Big Data. LNCS, vol. 9714, pp. 342–349. Springer, Heidelberg (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Youcef Abdelsadek
    • 1
    Email author
  • Kamel Chelghoum
    • 1
  • Francine Herrmann
    • 1
  • Imed Kacem
    • 1
  • Benoît Otjacques
    • 2
  1. 1.Laboratoire de Conception, Optimisation et Modélisation des SystèmesUniversité de LorraineMetzFrance
  2. 2.e-Science Research Unit, Environmental Research and Innovation LuxembourgInstitute of Science and TechnologyBelvauxLuxembourg

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