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

Abstract

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.

Published in at http://dx.doi.org/10.1214/10-AOAS359 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (3)

• Modularity and community structure in networks
• Mixture models and exploratory analysis in networks
• Uncovering latent structure in valued graphs: A variational approach

Cited by in corpus (5)

• Model-based clustering of large networks
• Mixed-Membership of Experts Stochastic Blockmodel
• Convergence of the groups posterior distribution in latent or stochastic block models
• New consistent and asymptotically normal estimators for random graph mixture models
• Network inference and community detection, based on covariance matrices, correlations and test statistics from arbitrary distributions