Machine learning meets complex networks via coalescent embedding in the hyperbolic space

Mapping complex networks to underlying geometric spaces can help understand the structure of networked systems. Here the authors propose a class of machine learning algorithms for efficient embedding of large real networks to the hyperbolic space, with potential impact on big network data analysis.

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Autores principales:	Alessandro Muscoloni, Josephine Maria Thomas, Sara Ciucci, Ginestra Bianconi, Carlo Vittorio Cannistraci
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/d1158773a17e4a07802f5e3302700f6a
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spelling	oai:doaj.org-article:d1158773a17e4a07802f5e3302700f6a2021-12-02T17:06:17ZMachine learning meets complex networks via coalescent embedding in the hyperbolic space10.1038/s41467-017-01825-52041-1723https://doaj.org/article/d1158773a17e4a07802f5e3302700f6a2017-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-01825-5https://doaj.org/toc/2041-1723Mapping complex networks to underlying geometric spaces can help understand the structure of networked systems. Here the authors propose a class of machine learning algorithms for efficient embedding of large real networks to the hyperbolic space, with potential impact on big network data analysis.Alessandro MuscoloniJosephine Maria ThomasSara CiucciGinestra BianconiCarlo Vittorio CannistraciNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-19 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Alessandro Muscoloni Josephine Maria Thomas Sara Ciucci Ginestra Bianconi Carlo Vittorio Cannistraci Machine learning meets complex networks via coalescent embedding in the hyperbolic space
description	Mapping complex networks to underlying geometric spaces can help understand the structure of networked systems. Here the authors propose a class of machine learning algorithms for efficient embedding of large real networks to the hyperbolic space, with potential impact on big network data analysis.
format	article
author	Alessandro Muscoloni Josephine Maria Thomas Sara Ciucci Ginestra Bianconi Carlo Vittorio Cannistraci
author_facet	Alessandro Muscoloni Josephine Maria Thomas Sara Ciucci Ginestra Bianconi Carlo Vittorio Cannistraci
author_sort	Alessandro Muscoloni
title	Machine learning meets complex networks via coalescent embedding in the hyperbolic space
title_short	Machine learning meets complex networks via coalescent embedding in the hyperbolic space
title_full	Machine learning meets complex networks via coalescent embedding in the hyperbolic space
title_fullStr	Machine learning meets complex networks via coalescent embedding in the hyperbolic space
title_full_unstemmed	Machine learning meets complex networks via coalescent embedding in the hyperbolic space
title_sort	machine learning meets complex networks via coalescent embedding in the hyperbolic space
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/d1158773a17e4a07802f5e3302700f6a
work_keys_str_mv	AT alessandromuscoloni machinelearningmeetscomplexnetworksviacoalescentembeddinginthehyperbolicspace AT josephinemariathomas machinelearningmeetscomplexnetworksviacoalescentembeddinginthehyperbolicspace AT saraciucci machinelearningmeetscomplexnetworksviacoalescentembeddinginthehyperbolicspace AT ginestrabianconi machinelearningmeetscomplexnetworksviacoalescentembeddinginthehyperbolicspace AT carlovittoriocannistraci machinelearningmeetscomplexnetworksviacoalescentembeddinginthehyperbolicspace
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Machine learning meets complex networks via coalescent embedding in the hyperbolic space

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