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)


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.


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



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


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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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