Detecting Stable Communities in Link Streams at Multiple Temporal Scales

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

1 ESI - École Nationale Supérieure d'Informatique [Alger]
2 DM2L - Data Mining and Machine Learning
3 IXXI - Institut Rhône-Alpin des systèmes complexes
4 CERIST - Centre de recherche sur l'Information Scientifique et Technique

Link streams model interactions over time in a wide range of fields. Under this model, the challenge is to mine efficiently both temporal and topological structures. Community detection and change point detection are one of the most powerful tools to analyze such evolving interactions. In this paper, we build on both to detect stable community structures by identifying change points within meaningful communities. Unlike existing dynamic community detection algorithms, the proposed method is able to discover stable communities efficiently at multiple temporal scales. We test the effectiveness of our method on synthetic networks, and on high-resolution time-varying networks of contacts drawn from real social networks.

Domaines: Réseaux sociaux et d'information [cs.SI]; Apprentissage [cs.LG]

Souâad Boudebza, Rémy Cazabet, Omar Nouali, Faiçal Azouaou. Detecting Stable Communities in Link Streams at Multiple Temporal Scales. LEG@ECML-PKDD 2019 - Third International Workshop on Advances in Managing and Mining Large Evolving Graphs, Sep 2019, Wurzburg, Germany. ⟨10.1007/978-3-030-43823-4_30⟩. ⟨hal-03261697⟩