Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm

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 α-β models. Our purpose is to give a parti...

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Main Authors: Marianna Bolla, Ahmed Elbanna
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2015/657965
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author Marianna Bolla
Ahmed Elbanna
author_facet Marianna Bolla
Ahmed Elbanna
author_sort Marianna Bolla
collection DOAJ
description 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 α-β 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.
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institution Kabale University
issn 1687-952X
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language English
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spelling doaj-art-66506a41a8d54c42bb96841f5a74ee302025-08-20T03:34:17ZengWileyJournal of Probability and Statistics1687-952X1687-95382015-01-01201510.1155/2015/657965657965Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM AlgorithmMarianna Bolla0Ahmed Elbanna1Institute of Mathematics, Budapest University of Technology and Economics, Műegyetem Rakpart 3, Budapest 1111, HungaryInstitute of Mathematics, Budapest University of Technology and Economics, Műegyetem Rakpart 3, Budapest 1111, HungaryWe 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 α-β 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.http://dx.doi.org/10.1155/2015/657965
spellingShingle Marianna Bolla
Ahmed Elbanna
Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm
Journal of Probability and Statistics
title Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm
title_full Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm
title_fullStr Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm
title_full_unstemmed Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm
title_short Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm
title_sort estimating parameters of a probabilistic heterogeneous block model via the em algorithm
url http://dx.doi.org/10.1155/2015/657965
work_keys_str_mv AT mariannabolla estimatingparametersofaprobabilisticheterogeneousblockmodelviatheemalgorithm
AT ahmedelbanna estimatingparametersofaprobabilisticheterogeneousblockmodelviatheemalgorithm