[Séminaire] A stochastic block model for interaction lengths

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Publié le 9 octobre 2020 Mis à jour le 13 octobre 2020

le 19 novembre 2020

Seminar held remotely by Michael Fop (UCD - University College Dublin) on November 19, 2020 at 10:00
London bike sharing data
London bike sharing data

A stochastic block model for interaction lengths

Speaker: Michael Fop, Lecturer/Assistant Professor in Statistics, School of Mathematics and Statistics, University College Dublin.

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.

Due to the current pandemic, this seminar will be held remotely via Microsoft Teams.

To register, please send an email to Marco.Corneli@univ-cotedazur.

*Image caption:
London bike sharing data. Left: Bike stations in London, where two bike stations are linked whenever there is at least one bike moving from one to the other. Right: Cluster allocations of the bike stations, matching relevant areas of London (colors denote the group allocation of each station).