Simplifying functional network representation and interpretation through causality clustering

Abstract Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain....

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Autor principal:	Massimiliano Zanin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f51d358322cc4051a8b4c04953a2ec79
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spelling	oai:doaj.org-article:f51d358322cc4051a8b4c04953a2ec792021-12-02T16:30:10ZSimplifying functional network representation and interpretation through causality clustering10.1038/s41598-021-94797-y2045-2322https://doaj.org/article/f51d358322cc4051a8b4c04953a2ec792021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94797-yhttps://doaj.org/toc/2045-2322Abstract Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain. They also present drawbacks, one of the most important being the inherent difficulty in representing and interpreting the resulting structures for large number of nodes and links. I here propose a causality clustering approach, based on grouping nodes into clusters according to their similarity in the overall information dynamics, the latter one being measured by a causality metric. The whole system can then arbitrarily be simplified, with nodes being grouped in e.g. sources, brokers and sinks of information. The advantages and limitations of the proposed approach are discussed using a set of synthetic and real-world data sets, the latter ones representing two neuroscience and technological problems.Massimiliano ZaninNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Massimiliano Zanin Simplifying functional network representation and interpretation through causality clustering
description	Abstract Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain. They also present drawbacks, one of the most important being the inherent difficulty in representing and interpreting the resulting structures for large number of nodes and links. I here propose a causality clustering approach, based on grouping nodes into clusters according to their similarity in the overall information dynamics, the latter one being measured by a causality metric. The whole system can then arbitrarily be simplified, with nodes being grouped in e.g. sources, brokers and sinks of information. The advantages and limitations of the proposed approach are discussed using a set of synthetic and real-world data sets, the latter ones representing two neuroscience and technological problems.
format	article
author	Massimiliano Zanin
author_facet	Massimiliano Zanin
author_sort	Massimiliano Zanin
title	Simplifying functional network representation and interpretation through causality clustering
title_short	Simplifying functional network representation and interpretation through causality clustering
title_full	Simplifying functional network representation and interpretation through causality clustering
title_fullStr	Simplifying functional network representation and interpretation through causality clustering
title_full_unstemmed	Simplifying functional network representation and interpretation through causality clustering
title_sort	simplifying functional network representation and interpretation through causality clustering
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f51d358322cc4051a8b4c04953a2ec79
work_keys_str_mv	AT massimilianozanin simplifyingfunctionalnetworkrepresentationandinterpretationthroughcausalityclustering
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Simplifying functional network representation and interpretation through causality clustering

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