Evaluation Metrics for Overlapping Community Detection

Safa El Ayeb (1, 2) , Baptiste Hemery (2) , Fabrice Jeanne (2) , Estelle Pawlowski Cherrier (1) , Christophe Charrier (1)

1 Equipe SAFE - Laboratoire GREYC - UMR6072
2 Orange Labs [Caen]

Networks have provided a representation for a wide range of real systems, including communication flow, money transfer or biological systems, to mention just a few. Communities represent fundamental structures for understanding the organization of real-world networks. Uncovering coherent groups in these networks is the goal of community detection. A community is a mesoscopic structure with nodes heavily connected within their groups by comparison to the nodes in other groups. Communities might also overlap as they may share one or multiple nodes. Evaluating the results of a community detection algorithm is an equally important task. This paper introduces metrics for evaluating overlapping community detection. The idea of introducing new metrics comes from the lack of efficiency and adequacy of state-of-the-art metrics for overlapping communities. The new metrics are tested both on simulated data and standard datasets and are compared with existing metrics.

Mots clés: Social Network Analysis Overlapping community detection evaluation metric; Social Network Analysis; Overlapping community detection; evaluation metric

Domaines: Analyse de données, Statistiques et Probabilités [physics.data-an]; Réseaux sociaux et d'information [cs.SI]

Safa El Ayeb, Baptiste Hemery, Fabrice Jeanne, Estelle Pawlowski Cherrier, Christophe Charrier. Evaluation Metrics for Overlapping Community Detection. 2022 IEEE 47th Conference on Local Computer Networks (LCN), Sep 2022, Edmonton, Canada. pp.355-358, ⟨10.1109/LCN53696.2022.9843473⟩. ⟨hal-03948984⟩