An iterative clustering algorithm for the ...
Document type :
Communication dans un congrès avec actes
Title :
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
Author(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]
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]
Conference title :
Proceedings of the 39th International Conference on Machine Learning
City :
Baltimore
Country :
Etats-Unis d'Amérique
Start date of the conference :
2022-07-17
HAL domain(s) :
Mathématiques [math]
Statistiques [stat]
Statistiques [stat]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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