OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks

Souâad Boudebza , Rémy Cazabet (1, 2, 3, 4) , Faiçal Azouaou (5) , Omar Nouali (6)

1 DM2L - Data Mining and Machine Learning
2 Université de Lyon
3 UCBL - Université Claude Bernard Lyon 1
4 ComplexNetworks
5 ESI - École Nationale Supérieure d'Informatique [Alger]
6 LPL - Laboratoire Parole et Langage

Community structure is one of the most prominent features of complex networks. Community structure detection is of great importance to provide insights into the network structure and functionalities. Most proposals focus on static networks. However, finding communities in a dynamic network is even more challenging, especially when communities overlap with each other. In this article , we present an online algorithm, called OLCPM, based on clique percolation and label propagation methods. OLCPM can detect overlapping communities and works on temporal networks with a fine granularity. By locally updating the community structure, OLCPM delivers significant improvement in running time compared with previous clique percolation techniques. The experimental results on both synthetic and real-world networks illustrate the effectiveness of the method.

Mots clés: Community Detection; Overlapping; Social Network; Clique; Temporal Network; Dynamic; Label Propagation

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

Souâad Boudebza, Rémy Cazabet, Faiçal Azouaou, Omar Nouali. OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks. Computer Communications, In press, ⟨10.1016/j.comcom.2018.04.003⟩. ⟨hal-01761341⟩