Interpreting communities based on the evolution of a dynamic attributed network

Günce Orman 1, * Vincent Labatut 2, * Marc Plantevit 3 Jean-François Boulicaut 3, *
* Auteur correspondant
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Many methods have been proposed to detect communities , not only in plain, but also in attributed, directed or even dynamic complex networks. From the modeling point of view, to be of some utility, the community structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the type of data they handle, or by the nature of the results they output. In this work, we see the interpretation of communities as a problem independent from the detection process, consisting in identifying the most characteristic features of communities. We give a formal definition of this problem and propose a method to solve it. To this aim, we first define a sequence-based representation of networks, combining temporal information, community structure, 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 study the performance of our method on artificially generated dynamic attributed networks. We also empirically validate our framework on real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions.
Liste complète des métadonnées

Littérature citée [40 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01163778
Contributeur : Vincent Labatut <>
Soumis le : lundi 15 juin 2015 - 14:56:30
Dernière modification le : jeudi 19 avril 2018 - 14:38:06
Document(s) archivé(s) le : mardi 25 avril 2017 - 08:15:03

Fichiers

preprint.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale - Pas de modification 4.0 International License

Identifiants

Citation

Günce Orman, Vincent Labatut, Marc Plantevit, Jean-François Boulicaut. Interpreting communities based on the evolution of a dynamic attributed network. Social Network Analysis and Mining, Springer, 2015, 5, pp.20. 〈http://link.springer.com/article/10.1007%2Fs13278-015-0262-4〉. 〈10.1007/s13278-015-0262-4〉. 〈hal-01163778〉

Partager

Métriques

Consultations de la notice

691

Téléchargements de fichiers

132