Likelihood-based approach to discriminate mixtures of network models that vary in time

Abstract Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closur...

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Autores principales:	Naomi A. Arnold, Raul J. Mondragón, Richard G. Clegg
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/87bf7a855812485985f136472c5354ad
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spelling	oai:doaj.org-article:87bf7a855812485985f136472c5354ad2021-12-02T13:20:21ZLikelihood-based approach to discriminate mixtures of network models that vary in time10.1038/s41598-021-84085-02045-2322https://doaj.org/article/87bf7a855812485985f136472c5354ad2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84085-0https://doaj.org/toc/2045-2322Abstract Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.Naomi A. ArnoldRaul J. MondragónRichard G. CleggNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naomi A. Arnold Raul J. Mondragón Richard G. Clegg Likelihood-based approach to discriminate mixtures of network models that vary in time
description	Abstract Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.
format	article
author	Naomi A. Arnold Raul J. Mondragón Richard G. Clegg
author_facet	Naomi A. Arnold Raul J. Mondragón Richard G. Clegg
author_sort	Naomi A. Arnold
title	Likelihood-based approach to discriminate mixtures of network models that vary in time
title_short	Likelihood-based approach to discriminate mixtures of network models that vary in time
title_full	Likelihood-based approach to discriminate mixtures of network models that vary in time
title_fullStr	Likelihood-based approach to discriminate mixtures of network models that vary in time
title_full_unstemmed	Likelihood-based approach to discriminate mixtures of network models that vary in time
title_sort	likelihood-based approach to discriminate mixtures of network models that vary in time
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/87bf7a855812485985f136472c5354ad
work_keys_str_mv	AT naomiaarnold likelihoodbasedapproachtodiscriminatemixturesofnetworkmodelsthatvaryintime AT rauljmondragon likelihoodbasedapproachtodiscriminatemixturesofnetworkmodelsthatvaryintime AT richardgclegg likelihoodbasedapproachtodiscriminatemixturesofnetworkmodelsthatvaryintime
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Likelihood-based approach to discriminate mixtures of network models that vary in time

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