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  • STCO_paper_v3

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9788-9

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Dynamic stochastic block models: Parameter estimation and detection of changes in community structure

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>11/2018
<mark>Journal</mark>Statistics and Computing
Issue number6
Volume28
Number of pages13
Pages (from-to)1201-1213
Publication statusPublished
Early online date2/11/17
Original languageEnglish

Abstract

The stochastic block model (SBM) is widely used for modelling network data by assigning individuals (nodes) to communities (blocks) with the probability of an edge existing between individuals depending upon community membership.

In this paper we introduce an autoregressive extension of the SBM. This is based on continuous time Markovian edge dynamics. The model is appropriate for networks evolving over time and allows for edges to turn on and off. Moreover, we allow for the movement of individuals between communities. An effective reversible jump Markov chain Monte Carlo algorithm is introduced for sampling jointly from the posterior distribution of the community parameters and the number and location of changes in community membership. The algorithm is successfully applied to a network of mice.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9788-9