Fast community structure local uncovering by independent vertex-centred process

Maël Canu (1) , Marcin Detyniecki (2, 1) , Marie-Jeanne Lesot (1) , Adrien Revault d'Allonnes (3)

1 LFI - Learning, Fuzzy and Intelligent systems
2 IBS PAN - Systems Research Institute [Warsaw]
3 LIASD - Laboratoire d'Informatique Avancée de Saint-Denis

This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours. A decentralised external process then unveils the community structure. The relevance of the proposed method is experimentally shown on both artificial and real data.

Mots clés: community detection; social networks; graph mining; communications; networks; distributed computing

Domaines: Informatique mobile; Réseaux et télécommunications [cs.NI]; Réseaux sociaux et d'information [cs.SI]; Modélisation et simulation

Maël Canu, Marcin Detyniecki, Marie-Jeanne Lesot, Adrien Revault d'Allonnes. Fast community structure local uncovering by independent vertex-centred process. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE; ACM, Aug 2015, Paris, France. pp.823-830, ⟨10.1145/2808797.2808866⟩. ⟨hal-01194324⟩