Configuration models as an urn problem

Abstract A fundamental issue of network data science is the ability to discern observed features that can be expected at random from those beyond such expectations. Configuration models play a crucial role there, allowing us to compare observations against degree-corrected null-models. Nonetheless,...

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Autores principales:	Giona Casiraghi, Vahan Nanumyan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/884e3af5cceb4c709975bc5897ca787e
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spelling	oai:doaj.org-article:884e3af5cceb4c709975bc5897ca787e2021-12-02T14:33:51ZConfiguration models as an urn problem10.1038/s41598-021-92519-y2045-2322https://doaj.org/article/884e3af5cceb4c709975bc5897ca787e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92519-yhttps://doaj.org/toc/2045-2322Abstract A fundamental issue of network data science is the ability to discern observed features that can be expected at random from those beyond such expectations. Configuration models play a crucial role there, allowing us to compare observations against degree-corrected null-models. Nonetheless, existing formulations have limited large-scale data analysis applications either because they require expensive Monte-Carlo simulations or lack the required flexibility to model real-world systems. With the generalized hypergeometric ensemble, we address both problems. To achieve this, we map the configuration model to an urn problem, where edges are represented as balls in an appropriately constructed urn. Doing so, we obtain the generalized hypergeometric ensemble of random graphs: a random graph model reproducing and extending the properties of standard configuration models, with the critical advantage of a closed-form probability distribution.Giona CasiraghiVahan NanumyanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Giona Casiraghi Vahan Nanumyan Configuration models as an urn problem
description	Abstract A fundamental issue of network data science is the ability to discern observed features that can be expected at random from those beyond such expectations. Configuration models play a crucial role there, allowing us to compare observations against degree-corrected null-models. Nonetheless, existing formulations have limited large-scale data analysis applications either because they require expensive Monte-Carlo simulations or lack the required flexibility to model real-world systems. With the generalized hypergeometric ensemble, we address both problems. To achieve this, we map the configuration model to an urn problem, where edges are represented as balls in an appropriately constructed urn. Doing so, we obtain the generalized hypergeometric ensemble of random graphs: a random graph model reproducing and extending the properties of standard configuration models, with the critical advantage of a closed-form probability distribution.
format	article
author	Giona Casiraghi Vahan Nanumyan
author_facet	Giona Casiraghi Vahan Nanumyan
author_sort	Giona Casiraghi
title	Configuration models as an urn problem
title_short	Configuration models as an urn problem
title_full	Configuration models as an urn problem
title_fullStr	Configuration models as an urn problem
title_full_unstemmed	Configuration models as an urn problem
title_sort	configuration models as an urn problem
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/884e3af5cceb4c709975bc5897ca787e
work_keys_str_mv	AT gionacasiraghi configurationmodelsasanurnproblem AT vahannanumyan configurationmodelsasanurnproblem
_version_	1718391141675565056

Configuration models as an urn problem

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