Core decomposition in Directed Networks: Kernelization and Strong Connectivity

Vincent Levorato (1, 2)

1 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans
2 LINEACT - Laboratoire d'Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires

In this paper, we propose a method allowing decomposition of directed networks into cores, which final objective is the detection of communities.We based our approach on the fact that a community should be composed of elements having communication in both directions. Therefore, we propose a method based on digraph kernelization and strongly p-connected components. By identifying cores, one can use based-centers clustering methods to generate full communities. Some experiments have been made on three real-world networks, and have been evaluated using the V-Measure, having a more precise analysis through its two sub-measures: homogeneity and completeness. Our work proposes different directions about the use of kernelization into structure analysis, and strong connectivity concept as an alternative to modularity optimization.

Mots clés: cores; communities detection; directed networks; kernelization; strong connectivity; graph theory; clustering

Domaines: Modélisation et simulation; Algorithme et structure de données [cs.DS]; Réseaux sociaux et d'information [cs.SI]

Vincent Levorato. Core decomposition in Directed Networks: Kernelization and Strong Connectivity. Complex Networks, Mar 2014, Bologne, Italy. pp.129-140, ⟨10.1007/978-3-319-05401-8_13⟩. ⟨hal-00961165⟩