CRUZ GOMEZ Juan David1, BOTHOREL Cécile1, POULET François2
Communication dans une conférence avec acte
International Conference on Computational Aspects of Social Networks, Salamanca : 19-21 october 2011, Salamanca, Spain, 2011, pp. 163-168, ISBN 9781457711312
Social network analysis has become a major subject in recent times, bringing also several challenges in the computer science field. One aspect of the social network analysis is the community detection problem, which is seen as a graph clustering problem. However, social networks are more than a graph, they have an interesting amount of information derived from its social aspect, such as profile information, content sharing and annotations, among others. Most of the community detection algorithms use only the structure of the network, i.e., the graph. In this paper we propose a new method which uses the semantic information along with the network structure in the community detection process. Thus, our method combines an algorithm for optimizing modularity and an entropy-based data clustering algorithm, which tries to find a partition with low entropy and keeping in mind the modularity.
1 : LUSSI - Dépt. Logique des Usages, Sciences Sociales et de l'Information (Institut Mines-Télécom-Télécom Bretagne-UEB)
2 : IRISA - Institut de recherche en informatique et systèmes aléatoires (UMR CNRS 6074 - Université de Rennes 1 - INRIA - INSA de Rennes - ENS de Cachan - Télécom Bretagne - Université de Bretagne Sud)
Social Networks Analysis, Graph Clustering, Community Detection, Entropy