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Choosing the number of groups in a latent stochastic block model for dynamic networks
Rastelli, Riccardo; Latouche, Pierre; Friel, Nial
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 stochastic 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. Science Foundation Ireland Insight Research Centre 2 year embargo to cover until published, checkdate -AC
Keyword(s): Stochastic block models; Dynamics networks; Greedy optimisation; Bayesian inference; Integrated completed likelihood
Publication Date:
2019
Type: Journal article
Peer-Reviewed: Unknown
Language(s): English
Institution: University College Dublin
Publisher(s): Cambridge University Press
First Indexed: 2019-05-11 06:31:42 Last Updated: 2019-05-11 06:31:42