SPONGE: A generalized eigenproblem for ...
Type de document :
Communication dans un congrès avec actes
Titre :
SPONGE: A generalized eigenproblem for clustering signed networks
Auteur(s) :
Cucuringu, Mihai [Auteur]
University of Oxford
Davies, Peter [Auteur]
University of Warwick [Coventry]
Glielmo, Aldo [Auteur]
King‘s College London
Tyagi, Hemant [Auteur]
MOdel for Data Analysis and Learning [MODAL]
University of Oxford
Davies, Peter [Auteur]
University of Warwick [Coventry]
Glielmo, Aldo [Auteur]
King‘s College London
Tyagi, Hemant [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Titre de la manifestation scientifique :
AISTATS
Ville :
Okinawa
Pays :
Japon
Date de début de la manifestation scientifique :
2019-04-16
Discipline(s) HAL :
Statistiques [stat]/Autres [stat.ML]
Résumé en anglais : [en]
We introduce a principled and theoretically sound spectral method for k-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values. Our approach is motivated by social ...
Lire la suite >We introduce a principled and theoretically sound spectral method for k-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values. Our approach is motivated by social balance theory, where the task of clustering aims to decompose the network into dis-joint groups, such that individuals within the same group are connected by as many positive edges as possible, while individuals from different groups are connected by as many negative edges as possible. Our algorithm relies on a generalized eigenproblem formulation inspired by recent work on constrained clustering. We provide theoretical guarantees for our approach in the setting of a signed stochastic block model, by leveraging tools from matrix perturbation theory and random matrix theory. An extensive set of numerical experiments on both synthetic and real data shows that our approach compares favorably with state-of-the-art methods for signed clustering , especially for large number of clusters and sparse measurement graphs.Lire moins >
Lire la suite >We introduce a principled and theoretically sound spectral method for k-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values. Our approach is motivated by social balance theory, where the task of clustering aims to decompose the network into dis-joint groups, such that individuals within the same group are connected by as many positive edges as possible, while individuals from different groups are connected by as many negative edges as possible. Our algorithm relies on a generalized eigenproblem formulation inspired by recent work on constrained clustering. We provide theoretical guarantees for our approach in the setting of a signed stochastic block model, by leveraging tools from matrix perturbation theory and random matrix theory. An extensive set of numerical experiments on both synthetic and real data shows that our approach compares favorably with state-of-the-art methods for signed clustering , especially for large number of clusters and sparse measurement graphs.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
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