Detecting informative higher-order interactions in statistically validated hypergraphs

The increasing availability of new data on biological and sociotechnical systems highlights the importance of well grounded filtering techniques to separate meaningful interactions from noise. In this work the authors propose the first method to detect informative connections of any order in statist...

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Autores principales:	Federico Musciotto, Federico Battiston, Rosario N. Mantegna
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/1ce10e574e424627ab206e9d3d8dedc3
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spelling	oai:doaj.org-article:1ce10e574e424627ab206e9d3d8dedc32021-12-02T18:13:53ZDetecting informative higher-order interactions in statistically validated hypergraphs10.1038/s42005-021-00710-42399-3650https://doaj.org/article/1ce10e574e424627ab206e9d3d8dedc32021-09-01T00:00:00Zhttps://doi.org/10.1038/s42005-021-00710-4https://doaj.org/toc/2399-3650The increasing availability of new data on biological and sociotechnical systems highlights the importance of well grounded filtering techniques to separate meaningful interactions from noise. In this work the authors propose the first method to detect informative connections of any order in statistically validated hypergraphs, showing on synthetic benchmarks and real-world systems that the highlighted hyperlinks are more informative than those extracted with traditional pairwise approaches.Federico MusciottoFederico BattistonRosario N. MantegnaNature PortfolioarticleAstrophysicsQB460-466PhysicsQC1-999ENCommunications Physics, Vol 4, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Astrophysics QB460-466 Physics QC1-999
spellingShingle	Astrophysics QB460-466 Physics QC1-999 Federico Musciotto Federico Battiston Rosario N. Mantegna Detecting informative higher-order interactions in statistically validated hypergraphs
description	The increasing availability of new data on biological and sociotechnical systems highlights the importance of well grounded filtering techniques to separate meaningful interactions from noise. In this work the authors propose the first method to detect informative connections of any order in statistically validated hypergraphs, showing on synthetic benchmarks and real-world systems that the highlighted hyperlinks are more informative than those extracted with traditional pairwise approaches.
format	article
author	Federico Musciotto Federico Battiston Rosario N. Mantegna
author_facet	Federico Musciotto Federico Battiston Rosario N. Mantegna
author_sort	Federico Musciotto
title	Detecting informative higher-order interactions in statistically validated hypergraphs
title_short	Detecting informative higher-order interactions in statistically validated hypergraphs
title_full	Detecting informative higher-order interactions in statistically validated hypergraphs
title_fullStr	Detecting informative higher-order interactions in statistically validated hypergraphs
title_full_unstemmed	Detecting informative higher-order interactions in statistically validated hypergraphs
title_sort	detecting informative higher-order interactions in statistically validated hypergraphs
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1ce10e574e424627ab206e9d3d8dedc3
work_keys_str_mv	AT federicomusciotto detectinginformativehigherorderinteractionsinstatisticallyvalidatedhypergraphs AT federicobattiston detectinginformativehigherorderinteractionsinstatisticallyvalidatedhypergraphs AT rosarionmantegna detectinginformativehigherorderinteractionsinstatisticallyvalidatedhypergraphs
_version_	1718378437818712064

Detecting informative higher-order interactions in statistically validated hypergraphs

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