Graph Mining and Communities Detection

Etienne Cuvelier (1) , Marie-Aude Aufaure (1)

1 MAS - Mathématiques Appliquées aux Systèmes - EA 4037

The incredible rising of on-line social networks gives a new and very strong interest to the set of techniques developed since several decades to mining graphs and social networks. In particularly community detection methods can bring very valuable informations about the structure of an existing social network in the Business Intelligence framework. In this chapter we give a large view, firstly of what could be a community in a social network, and then we list he most popular techniques to detect such communities. Some of these techniques were particularly developed in the SNA context, while other are adaptations of classical clustering techniques. We have sorted them in following an increasing complexity order, because with very big graphs the complexity can be decisive for the choice of an algorithm.

Mots clés: Graph Mining; Community Detection; Data Mining

Domaines: Machine Learning [stat.ML]

Etienne Cuvelier, Marie-Aude Aufaure. Graph Mining and Communities Detection. Aufaure, Marie-Aude; Zimanyi, Esteban. First European Summer School, eBISS 2011, Paris, France, July 3-8, 2011, Tutorial Lectures, Springer, pp.117-138, 2012, Lecture Notes in Business Information Processing. ⟨hal-00704356⟩