Titre :

An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees

Auteur(s) :

Braun, Guillaume [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Tyagi, Hemant [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]

Titre de la manifestation scientifique :

Proceedings of the 39th International Conference on Machine Learning

Ville :

Baltimore

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2022-07-17

Discipline(s) HAL :

Mathématiques [math]
Statistiques [stat]

Résumé en anglais : [en]

Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network ...
Lire la suite >Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information and the methods used to incorporate it optimally in clustering algorithms are relatively less understood. We propose a new iterative algorithm to cluster networks with side information for nodes (in the form of covariates) and show that our algorithm is optimal under the Contextual Symmetric Stochastic Block Model. Our algorithm can be applied to general Contextual Stochastic Block Models and avoids hyperparameter tuning in contrast to previously proposed methods. We confirm our theoretical results on synthetic data experiments where our algorithm significantly outperforms other methods, and show that it can also be applied to signed graphs. Finally we demonstrate the practical interest of our method on real data.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL