Choosing the number of groups in a latent stochastic block model for dynamic networks

Abstract : Latent stochastic block models are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper we extend the original stochas-tic block model by using a Markovian property to describe the evolution of nodes' cluster memberships over time. We recast the problem of clustering the nodes of the network into a model-based context, and show that the integrated completed likelihood can be evaluated analytically for a number of likelihood models. Then, we propose a scalable greedy algorithm to maximise this quantity, thereby estimating both the optimal partition and the ideal number of groups in a single inferential framework. Finally we propose applications of our methodology to both real and artificial datasets.
Type de document :
Pré-publication, Document de travail
Liste complète des métadonnées

Littérature citée [25 références]  Voir  Masquer  Télécharger
Contributeur : Pierre Latouche <>
Soumis le : mardi 9 mai 2017 - 13:59:30
Dernière modification le : lundi 27 novembre 2017 - 14:14:02
Document(s) archivé(s) le : jeudi 10 août 2017 - 13:19:39


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-01519850, version 1



Riccardo Rastelli, Pierre Latouche, Nial Friel. Choosing the number of groups in a latent stochastic block model for dynamic networks. 2017. 〈hal-01519850〉



Consultations de la notice


Téléchargements de fichiers