Skip to main content
SpringerLink
Log in

Towards Contextualizing Community Detection in Dynamic Social Networks

  • Conference paper
  • First Online:
Book cover Modeling and Using Context (CONTEXT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10257))

Abstract

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.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://dblp.uni-trier.de/.

  2. 2.

    https://fr-fr.facebook.com/.

  3. 3.

    https://www.linkedin.com/uas/login.

  4. 4.

    www.riadi.rnu.tn.

References

  1. Milioris, D.: Trend Detection and Information Propagation in Dynamic Social Networks. Doctoral dissertation, École Polytechnique (2015)

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  4. Newman, M.E.J.: Networks: An Introduction, 1st edn. Oxford University Press, Oxford (2010)

    Book  MATH  Google Scholar 

  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). rsif2012022

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Ngonmang, B., Viennet, E.: Dynamique des communautés par prédiction d’interactions dans les réseaux sociaux. EGC, 553–556 (2014)

    Google Scholar 

  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. Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033015 (2009)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  12. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 103 (2009)

    MathSciNet  Google Scholar 

  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

    Chapter  Google Scholar 

  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. Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)

    Article  Google Scholar 

  16. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. J. Phys. Rev. 69(2), 026113 (2004)

    Google Scholar 

  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)

    Article  Google Scholar 

  18. Kim, J., Lee, J.G.: Community detection in multi-layer graphs: a survey. ACM SIGMOD Rec. 44(3), 37–48 (2015)

    Article  Google Scholar 

  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. Combe, D.: Détection de communautés dans les réseaux d’information utilisant liens et attributs (2013)

    Google Scholar 

  21. Kanawati, R.: Détection de communautés dans les grands graphes d’interactions (multiplexes): état de l’art (2013)

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  26. Lebart, L., Maurineau, A., Piron, M.: Traitement des données statistiques. Dunod, Paris (1982)

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia

    Wala Rebhi, Nesrine Ben Yahia & Narjès Bellamine Ben Saoud

  2. Institut de Recherche En Informatique de Toulouse (IRIT), Toulouse, France

    Chihab Hanachi

Authors
  1. Wala Rebhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nesrine Ben Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Narjès Bellamine Ben Saoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chihab Hanachi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wala Rebhi .

Editor information

Editors and Affiliations

  1. Pierre and Marie Curie University , Paris, France

    Patrick Brézillon

  2. University of Maine , Orono, Maine, USA

    Roy Turner

  3. University of Genoa , Genoa, Italy

    Carlo Penco

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Rebhi, W., Yahia, N.B., Saoud, N.B.B., Hanachi, C. (2017). Towards Contextualizing Community Detection in Dynamic Social Networks. In: Brézillon, P., Turner, R., Penco, C. (eds) Modeling and Using Context. CONTEXT 2017. Lecture Notes in Computer Science(), vol 10257. Springer, Cham. https://doi.org/10.1007/978-3-319-57837-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57837-8_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57836-1

  • Online ISBN: 978-3-319-57837-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation