Nonnegative matrix factorization to find features in temporal networks

Ronan Hamon 1 Pierre Borgnat 1 Patrick Flandrin 1 Céline Robardet 2
1 Phys-ENS - Laboratoire de Physique de l'ENS Lyon
2 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Temporal networks describe a large variety of systems having a temporal evolution. Characterization and visualization of their evolution are often an issue especially when the amount of data becomes huge. We propose here an approach based on the duality between graphs and signals. Temporal networks are represented at each time instant by a collection of signals, whose spectral analysis reveals connection between frequency features and structure of the network. We use nonnegative matrix factorization (NMF) to find these frequency features and track them along time. Transforming back these features into subgraphs reveals the underlying structures which form a decomposition of the temporal network.
Type de document :
Communication dans un congrès
2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014, Florence, Italy. pp.SPTM-P4.1, 2014
Liste complète des métadonnées

Littérature citée [17 références]  Voir  Masquer  Télécharger
https://hal-ens-lyon.archives-ouvertes.fr/ensl-00989760
Contributeur : Ronan Hamon <>
Soumis le : lundi 12 mai 2014 - 13:56:31
Dernière modification le : jeudi 19 avril 2018 - 14:54:04
Document(s) archivé(s) le : mardi 12 août 2014 - 11:45:12

Fichier

abstract_icassp14.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : ensl-00989760, version 1

Collections

ENS-LYON | LIRIS | EC-LYON | UNIV-LYON2 | INSA-LYON | UNIV-LYON1

Citation

Ronan Hamon, Pierre Borgnat, Patrick Flandrin, Céline Robardet. Nonnegative matrix factorization to find features in temporal networks. 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014, Florence, Italy. pp.SPTM-P4.1, 2014. 〈ensl-00989760〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

263

Téléchargements de fichiers

471

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD