Community detection methods can discover better structural clusters than ground-truth communities - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Community detection methods can discover better structural clusters than ground-truth communities

Résumé

Community detection emerged as an important exploratory task in complex networks analysis across many scientific domains. Many methods have been proposed to solve this problem, each one with its own mechanism and sometimes with a different notion of community. In this article, we bring most common methods in the literature together in a comparative approach and reveal their performances in both real-world networks and synthetic networks. Surprisingly, many of those methods discovered better communities than the declared ground-truth communities in terms of some topological goodness features, even on benchmarking networks with built-in communities. We illustrate different structural characteristics that these methods could identify in order to support users to choose an appropriate method according to their specific requirements on different structural qualities.
Fichier principal
Vignette du fichier
CRV_Webversion.pdf (2.47 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01577343 , version 1 (25-08-2017)

Identifiants

Citer

Vinh-Loc Dao, Cécile Bothorel, Philippe Lenca. Community detection methods can discover better structural clusters than ground-truth communities. 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Jul 2017, Sydney, Australia. ⟨10.1145/3110025.3110053⟩. ⟨hal-01577343⟩
318 Consultations
938 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More