Towards Contextualizing Community Detection in Dynamic Social Networks

  • Wala RebhiEmail author
  • Nesrine Ben Yahia
  • Narjès Bellamine Ben Saoud
  • Chihab Hanachi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10257)


With the growing number of users and the huge amount of information in dynamic social networks, contextualizing community detection has been a challenging task. Thus, modeling these social networks is a key issue for the process of contextualized community detection. In this work, we propose a temporal multiplex information graph-based model to represent dynamic social networks: we consider simultaneously the social network dynamicity, its structure (different social connections) and various members’ profiles so as to calculate similarities between “nodes” in each specific context. Finally a comparative study on a real social network shows the efficiency of our approach and illustrates practical uses.


Temporal multiplex information graph Dynamic social networks Contextualized community detection Modularity Inertia Similarity 


  1. 1.
    Milioris, D.: Trend Detection and Information Propagation in Dynamic Social Networks. Doctoral dissertation, École Polytechnique (2015)Google Scholar
  2. 2.
    Barabâsi, A.L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Phys. A: Stat. Mech. Appl. 311(3), 590–614 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Plantié, M., Crampes, M.: Survey on social community detection. In: Ramzan, N., van Zwol, R., Lee, J.-S., Clüver, K., Hua, X.-S. (eds.) Social Media Retrieval, pp. 65–85. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Newman, M.E.J.: Networks: An Introduction, 1st edn. Oxford University Press, Oxford (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Psorakis, I., Roberts, S.J., Rezek, I., Sheldon, B.C.: Inferring social network structure in ecological systems from spatio-temporal data streams. J. R. Soc. Interface 9, 1–10 (2012). rsif2012022CrossRefGoogle Scholar
  6. 6.
    Ford, D.A., Kaufman, J.H., Mesika, Y.: Modeling in space and time. In: Castillo-Chavez, C., Chen, H., Lober, W.B., Thurmond, M., Zeng, D. (eds.) Infectious Disease Informatics and Biosurveillance, vol. 27, pp. 191–206. Springer, Berlin (2011)CrossRefGoogle Scholar
  7. 7.
    Xu, A., Zheng, X.: Dynamic social network analysis using latent space model and an integrated clustering algorithm. In: Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2009, pp. 620–625. IEEE (2009)Google Scholar
  8. 8.
    Ngonmang, B., Viennet, E.: Dynamique des communautés par prédiction d’interactions dans les réseaux sociaux. EGC, 553–556 (2014)Google Scholar
  9. 9.
    Aynaud, T., Guillaume, J.-L.: Multi-step community detection and hierarchical time segmentation in evolving networks. In: Fifth SNA-KDD Workshop Social Network Mining and Analysis, in Conjunction with the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD (2011)Google Scholar
  10. 10.
    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
  11. 11.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 103 (2009)MathSciNetGoogle Scholar
  13. 13.
    Zimmermann, A., Lorenz, A., Oppermann, R.: An operational definition of context. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS (LNAI), vol. 4635, pp. 558–571. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-74255-5_42 CrossRefGoogle Scholar
  14. 14.
    Zainol, Z., Nakata, K.: Generic context ontology modelling: a review and framework. In: 2nd International Conference on Computer Technology and Development (ICCTD), pp. 126–130. IEEE (2010)Google Scholar
  15. 15.
    Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  16. 16.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. J. Phys. Rev. 69(2), 026113 (2004)Google Scholar
  17. 17.
    Blondel, V., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  18. 18.
    Kim, J., Lee, J.G.: Community detection in multi-layer graphs: a survey. ACM SIGMOD Rec. 44(3), 37–48 (2015)CrossRefGoogle Scholar
  19. 19.
    Moser, F., Ge, R., Ester, M.: Joint cluster analysis of attribute and relationship data without a-priori specification of the number of clusters. In: Dans Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 510–519 (2007)Google Scholar
  20. 20.
    Combe, D.: Détection de communautés dans les réseaux d’information utilisant liens et attributs (2013)Google Scholar
  21. 21.
    Kanawati, R.: Détection de communautés dans les grands graphes d’interactions (multiplexes): état de l’art (2013)Google Scholar
  22. 22.
    Boccaletti, S., Bianconi, G., Criado, R., Del Genio, C.I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., Zanin, M.: The structure and dynamics of multilayer networks. Phys. Rep. 544(1), 1–122 (2014)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kanawati, R.: Co-authorship link prediction in multiplex bibliographical networks. In: Multiplex Network Workshop - European Conference on Complex Systems (ECCS 2013) (2013)Google Scholar
  24. 24.
    Nguyen, N., Dinh, T., Xuan, Y., Thai, M.: Adaptive algorithms for detecting community structure in dynamic social networks. In: 2011 Proceedings of IEEE INFOCOM, pp. 2282–2290 (2011)Google Scholar
  25. 25.
    Ben Yahia, N., Bellamine, N., Ben Ghezala, H.: Community-based collaboration recommendation to support mixed decision making support. J. Decis. Syst. 23(3), 350–371 (2014)CrossRefGoogle Scholar
  26. 26.
    Lebart, L., Maurineau, A., Piron, M.: Traitement des données statistiques. Dunod, Paris (1982)Google Scholar
  27. 27.
    Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P.: Community structure in time-dependent, multiscale, and multiplex networks. science 328(5980), 876–878 (2010)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wala Rebhi
    • 1
    Email author
  • Nesrine Ben Yahia
    • 1
  • Narjès Bellamine Ben Saoud
    • 1
  • Chihab Hanachi
    • 2
  1. 1.RIADI Laboratory, National School of Computer SciencesUniversity of ManoubaManoubaTunisia
  2. 2.Institut de Recherche En Informatique de Toulouse (IRIT)ToulouseFrance

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