Weighted stochastic block model

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....

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Detalles Bibliográficos
Autores principales: Ng, Tin Lok James, Murphy, Thomas Brendan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608781/
https://www.ncbi.nlm.nih.gov/pubmed/34840548
http://dx.doi.org/10.1007/s10260-021-00590-6
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author Ng, Tin Lok James
Murphy, Thomas Brendan
author_facet Ng, Tin Lok James
Murphy, Thomas Brendan
author_sort Ng, Tin Lok James
collection PubMed
description 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.
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spelling pubmed-86087812021-11-24 Weighted stochastic block model Ng, Tin Lok James Murphy, Thomas Brendan Stat Methods Appt Original Paper 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. Springer Berlin Heidelberg 2021-09-13 2021 /pmc/articles/PMC8608781/ /pubmed/34840548 http://dx.doi.org/10.1007/s10260-021-00590-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Paper
Ng, Tin Lok James
Murphy, Thomas Brendan
Weighted stochastic block model
title Weighted stochastic block model
title_full Weighted stochastic block model
title_fullStr Weighted stochastic block model
title_full_unstemmed Weighted stochastic block model
title_short Weighted stochastic block model
title_sort weighted stochastic block model
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608781/
https://www.ncbi.nlm.nih.gov/pubmed/34840548
http://dx.doi.org/10.1007/s10260-021-00590-6
work_keys_str_mv AT ngtinlokjames weightedstochasticblockmodel
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