Estimating parameters of a probabilistic heterogeneous block model via the EM algorithm
type: Item
instance of: scholarly article
title: Estimating parameters of a probabilistic heterogeneous block model via the EM algorithm
zbMATH Open document ID: 1403.62094
DOI: 10.1155/2015/657965
author: Marianna Bolla
author: Ahmed Elbanna
published in: Journal of Probability and Statistics
publication date: 2018-08-14T00:00:00Z
review text: Summary: We introduce a semiparametric block model for graphs, where the within- and between-cluster edge probabilities are not constants within the blocks but are described by logistic type models, reminiscent of the 50-year-old Rasch model and the newly introduced \(\alpha\)-\(\beta\) models. Our purpose is to give a partition of the vertices of an observed graph so that the induced subgraphs and bipartite graphs obey these models, where their strongly interlaced parameters give multiscale evaluation of the vertices at the same time. In this way, a profoundly heterogeneous version of the stochastic block model is built via mixtures of the above submodels, while the parameters are estimated with a special EM iteration.
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 05C80
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 6917489
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
Wikidata QID: Q59114234
full work available at URL: https://doi.org/10.1155/2015/657965
OpenAlex ID: W2249264092
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description: scientific article; zbMATH DE number 6917489