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Towards Contextualizing Community Detection in Dynamic Social Networks

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.
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Submitted on : Thursday, October 4, 2018 - 3:32:08 PM
Last modification on : Friday, June 19, 2020 - 3:35:29 AM
Long-term archiving on: : Saturday, January 5, 2019 - 4:09:59 PM


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  • HAL Id : hal-01887896, version 1
  • OATAO : 19072


Wala Rebhi, Nesrine Ben Yahia, Narjès Bellamine Ben Saoud, Chihab Hanachi. Towards Contextualizing Community Detection in Dynamic Social Networks. 10th International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT 2017), Jun 2017, Paris, France. pp. 324-336. ⟨hal-01887896⟩



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