SPONGE: A generalized eigenproblem for clustering signed networks

Cucuringu, Mihai; Davies, Peter; Glielmo, Aldo; Tyagi, Hemant

Document type :

Communication dans un congrès avec actes

Title :

SPONGE: A generalized eigenproblem for clustering signed networks

Author(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]

Conference title :

AISTATS

City :

Okinawa

Country :

Japon

Start date of the conference :

2019-04-16

HAL domain(s) :

Statistiques [stat]/Autres [stat.ML]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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