A Method for Characterizing Communities in Dynamic Attributed Complex Networks - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

A Method for Characterizing Communities in Dynamic Attributed Complex Networks

Günce Keziban Orman
  • Fonction : Auteur
  • PersonId : 906373
Vincent Labatut
Marc Plantevit
Jean-François Boulicaut

Résumé

Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. In its simplest form, a community structure takes the form of a partition of the node set. From the modeling point of view, to be of some utility, this partition must then be characterized relatively to the properties of the studied system. However, if most of the existing works focus on defining methods for the detection of communities, only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either in the type of data they handle, or by the nature of the results they output. In this work, we propose a method to efficiently support such a characterization task. We first define a sequence-based representation of networks, combining temporal information, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We also show how to detect unusual behavior in a community, and highlight outliers. Finally, as an illustration, we apply our method to a network of scientific collaborations.
Fichier principal
Vignette du fichier
asonam.pdf (287.7 Ko) Télécharger le fichier
ASONAM Sunum.pdf (514.94 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01011913 , version 1 (25-06-2014)

Identifiants

Citer

Günce Keziban Orman, Vincent Labatut, Marc Plantevit, Jean-François Boulicaut. A Method for Characterizing Communities in Dynamic Attributed Complex Networks. IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), Aug 2014, Pékin, China. pp.481-484, ⟨10.1109/ASONAM.2014.6921629⟩. ⟨hal-01011913⟩
218 Consultations
253 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More