Détection des communautés par une méthode d’apprentissage automatique

Abstract

Community detection in networks plays an essential role in understanding their structures. The application of machine learning methods to community detection tasks in complex networks has attracted sustained attention in recent years, we propose in this thesis a new community detection approach based on k-means with the initialization of central nodes according to their densities and their degrees, the choice of the number of community k is made according to the best modularity. Our approach is efficient, simple and easy to implement. We compared our algorithm with some state-of-the-art algorithms on synthetic networks and real networks, with the evaluation measure : Modularity Q, and we obtain very acceptable results.