Rastelli, RiccardoRiccardoRastelliLatouche, PierrePierreLatoucheFriel, NialNialFriel2019-05-072019-05-072018 Cambr2018-11-15Network Sciencehttp://hdl.handle.net/10197/10302Latent 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.enThis article has been published in a revised form in Network Science https://doi.org/10.1017/nws.2018.19. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © Cambridge University Press.Stochastic block modelsDynamics networksGreedy optimisationBayesian inferenceIntegrated completed likelihoodJournal Article6446949310.1017/nws.2018.192018-09-26SFI/12/RC/228912/IP/1424https://creativecommons.org/licenses/by-nc-nd/3.0/ie/