Statistical inference for valued-edge networks: the generalized exponential random graph model.

Statistical inference for valued-edge networks: the generalized exponential random graph model.

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means...

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Autores principales: Bruce A Desmarais, Skyler J Cranmer
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2012
Materias:
Medicine
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Science
Q
Acceso en línea:https://doaj.org/article/9e41ba641cf04d34bb600bb00bcde5b0
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spelling oai:doaj.org-article:9e41ba641cf04d34bb600bb00bcde5b02021-11-18T07:29:50ZStatistical inference for valued-edge networks: the generalized exponential random graph model.1932-620310.1371/journal.pone.0030136https://doaj.org/article/9e41ba641cf04d34bb600bb00bcde5b02012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22276151/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.Bruce A DesmaraisSkyler J CranmerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 1, p e30136 (2012)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Bruce A Desmarais
Skyler J Cranmer
Statistical inference for valued-edge networks: the generalized exponential random graph model.
description Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.
format article
author Bruce A Desmarais
Skyler J Cranmer
author_facet Bruce A Desmarais
Skyler J Cranmer
author_sort Bruce A Desmarais
title Statistical inference for valued-edge networks: the generalized exponential random graph model.
title_short Statistical inference for valued-edge networks: the generalized exponential random graph model.
title_full Statistical inference for valued-edge networks: the generalized exponential random graph model.
title_fullStr Statistical inference for valued-edge networks: the generalized exponential random graph model.
title_full_unstemmed Statistical inference for valued-edge networks: the generalized exponential random graph model.
title_sort statistical inference for valued-edge networks: the generalized exponential random graph model.
publisher Public Library of Science (PLoS)
publishDate 2012
url https://doaj.org/article/9e41ba641cf04d34bb600bb00bcde5b0
work_keys_str_mv AT bruceadesmarais statisticalinferenceforvaluededgenetworksthegeneralizedexponentialrandomgraphmodel
AT skylerjcranmer statisticalinferenceforvaluededgenetworksthegeneralizedexponentialrandomgraphmodel
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