Strategies for online inference of model-based clustering in large and growing networks - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Annals of Applied Statistics Année : 2010

Strategies for online inference of model-based clustering in large and growing networks

H. Zanghi
  • Fonction : Auteur
C. Ambroise
  • Fonction : Auteur

Résumé

In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm, and the second on variational methods. These two strategies are compared with existing approaches on simulated and real data. We use the method to decipher the connexion structure of the political websphere during the US political campaign in 2008. We show that our online EM-based algorithms offer a good trade-off between precision and speed, when estimating parameters for mixture distributions in the context of random graphs.

Domaines

Autre [q-bio.OT]

Dates et versions

hal-00539318 , version 1 (24-11-2010)

Identifiants

Citer

H. Zanghi, Franck Picard, V. Miele, C. Ambroise. Strategies for online inference of model-based clustering in large and growing networks. Annals of Applied Statistics, 2010, 4 (2), pp.687-714. ⟨10.1214/10-AOAS359⟩. ⟨hal-00539318⟩
63 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More