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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)

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.

Keywords

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

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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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