Hidden network generating rules from partially observed complex networks

Understanding heterogeneous topological structures in real-world complex networks is challenged by the difficulty of describing their multifractal nature and inferring their generator rules. Here, the authors present a weighted multifractal graph model as a generative approach for studying the struc...

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Autores principales:	Ruochen Yang, Frederic Sala, Paul Bogdan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/2f61f0dfdd31485490ea652e17b6bb9e
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spelling	oai:doaj.org-article:2f61f0dfdd31485490ea652e17b6bb9e2021-12-02T19:04:19ZHidden network generating rules from partially observed complex networks10.1038/s42005-021-00701-52399-3650https://doaj.org/article/2f61f0dfdd31485490ea652e17b6bb9e2021-09-01T00:00:00Zhttps://doi.org/10.1038/s42005-021-00701-5https://doaj.org/toc/2399-3650Understanding heterogeneous topological structures in real-world complex networks is challenged by the difficulty of describing their multifractal nature and inferring their generator rules. Here, the authors present a weighted multifractal graph model as a generative approach for studying the structural properties of complex networks in realistic scenarios where only partial observational data is available or the input network is noisy, and demonstrate it on biological networks.Ruochen YangFrederic SalaPaul BogdanNature PortfolioarticleAstrophysicsQB460-466PhysicsQC1-999ENCommunications Physics, Vol 4, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Astrophysics QB460-466 Physics QC1-999
spellingShingle	Astrophysics QB460-466 Physics QC1-999 Ruochen Yang Frederic Sala Paul Bogdan Hidden network generating rules from partially observed complex networks
description	Understanding heterogeneous topological structures in real-world complex networks is challenged by the difficulty of describing their multifractal nature and inferring their generator rules. Here, the authors present a weighted multifractal graph model as a generative approach for studying the structural properties of complex networks in realistic scenarios where only partial observational data is available or the input network is noisy, and demonstrate it on biological networks.
format	article
author	Ruochen Yang Frederic Sala Paul Bogdan
author_facet	Ruochen Yang Frederic Sala Paul Bogdan
author_sort	Ruochen Yang
title	Hidden network generating rules from partially observed complex networks
title_short	Hidden network generating rules from partially observed complex networks
title_full	Hidden network generating rules from partially observed complex networks
title_fullStr	Hidden network generating rules from partially observed complex networks
title_full_unstemmed	Hidden network generating rules from partially observed complex networks
title_sort	hidden network generating rules from partially observed complex networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2f61f0dfdd31485490ea652e17b6bb9e
work_keys_str_mv	AT ruochenyang hiddennetworkgeneratingrulesfrompartiallyobservedcomplexnetworks AT fredericsala hiddennetworkgeneratingrulesfrompartiallyobservedcomplexnetworks AT paulbogdan hiddennetworkgeneratingrulesfrompartiallyobservedcomplexnetworks
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Hidden network generating rules from partially observed complex networks

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