A stochastic block model for interaction lengths

Riccardo Rastelli () and Michael Fop
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Riccardo Rastelli: University College Dublin
Michael Fop: University College Dublin

Advances in Data Analysis and Classification, 2020, vol. 14, issue 2, No 11, 485-512

Abstract: Abstract We propose a new stochastic block model that focuses on the analysis of interaction lengths in dynamic networks. The model does not rely on a discretization of the time dimension and may be used to analyze networks that evolve continuously over time. The framework relies on a clustering structure on the nodes, whereby two nodes belonging to the same latent group tend to create interactions and non-interactions of similar lengths. We introduce a variational expectation–maximization algorithm to perform inference, and adapt a widely used clustering criterion to perform model choice. Finally, we validate our methodology using simulated data experiments and showing two illustrative applications concerning face-to-face interaction data and a bike sharing network.

Keywords: Interaction lengths; Stochastic block model; Variational inference; Integrated completed likelihood; Social network analysis; 62H30; 91D30 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11634-020-00403-w

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