Modularity-based Sparse Soft Graph Clustering - Equipe Data, Intelligence and Graphs Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Modularity-based Sparse Soft Graph Clustering

Résumé

Clustering is a central problem in machine learning for which graph-based approaches have proven their efficiency. In this paper, we study a relaxation of the modularity maxi-mization problem, well-known in the graph partitioning literature. A solution of this relaxation gives to each element of the dataset a probability to belong to a given cluster, whereas a solution of the standard modular-ity problem is a partition. We introduce an efficient optimization algorithm to solve this relaxation, that is both memory efficient and local. Furthermore, we prove that our method includes, as a special case, the Louvain optimization scheme, a state-of-the-art technique to solve the traditional modularity problem. Experiments on both synthetic and real-world data illustrate that our approach provides meaningful information on various types of data.
Fichier principal
Vignette du fichier
aistats2019.pdf (1.73 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02005331 , version 1 (04-02-2019)

Identifiants

  • HAL Id : hal-02005331 , version 1

Citer

Alexandre Hollocou, Thomas Bonald, Marc Lelarge. Modularity-based Sparse Soft Graph Clustering. AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Apr 2019, Naha, Okinawa, Japan. ⟨hal-02005331⟩
373 Consultations
592 Téléchargements

Partager

Gmail Facebook X LinkedIn More