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Community detection has become a major active area of resear ch in recent years. A plethora of relevant methods have been implemented for dire cted graphs. Most of them focus on the density of links, and consider the relationship betwe n nodes as symmetric by ignoring links directionality during their clustering step, this le ading to non-semantic results. This paper propose an efficient method based on the extraction of kernel s through the distribution of triads in the graph, using Kernel Degree Clustering (KDC) a novel me tric to judge the quality of a community partitioning, demonstrated to yield superior r esults over other commonly used metrics like modularity in conformity with centrality. To v alidate our approach, we conduct experiments on some networks which show that it has better pe rformance over some of the other state-of-the-art methods and uncovers expected comm unities. MOTS-CLÉS : Réseaux orientés, Détection des communautés kernel, Clust ers basés sur la structure, Triade.

Nouvelle approche de clustering par kernel-pattern via la densité en triades