Weighted stochastic block model

Tin Lok James Ng () and Thomas Brendan Murphy
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Tin Lok James Ng: Trinity College Dublin
Thomas Brendan Murphy: University College Dublin

Statistical Methods & Applications, 2021, vol. 30, issue 5, No 5, 1365-1398

Abstract: Abstract We propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed. The proposed model is applied to simulated data and an illustrative data set.

Keywords: Weighted stochastic block model; Variational estimators; Maximum likelihood estimators; Consistency; Model selection (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10260-021-00590-6

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