Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space

Abstract Networks of disparate phenomena—be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions—exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal...

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Autor principal:	Keith Malcolm Smith
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8806a638a86c4cd889e213b8d3f79438
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spelling	oai:doaj.org-article:8806a638a86c4cd889e213b8d3f794382021-12-02T15:23:28ZExplaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space10.1038/s41598-021-81547-32045-2322https://doaj.org/article/8806a638a86c4cd889e213b8d3f794382021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81547-3https://doaj.org/toc/2045-2322Abstract Networks of disparate phenomena—be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions—exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal node fitness (surface) factor and a latent Euclidean space-embedded node similarity (depth) factor. Building on recurring trends in the literature, the theory asserts that links arise due to individualistic as well as dyadic information and that important dyadic information making up the so-called depth factor is obscured by this essentially non-dyadic information making up the surface factor. Modelling based on this theory considerably outperforms popular power-law fitness and hyperbolic geometry explanations across 110 networks. Importantly, the degree distributions of the model resemble power-laws at small densities and log-normal distributions at larger densities, posing a reconciliatory solution to the long-standing debate on the nature and existence of scale-free networks. Validating this theory, a surface factor inversion approach on an economic world city network and an fMRI connectome results in considerably more geometrically aligned nearest neighbour networks, as is hypothesised to be the case for the depth factor. This establishes new foundations from which to understand, analyse, deconstruct and interpret network phenomena.Keith Malcolm SmithNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Keith Malcolm Smith Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space
description	Abstract Networks of disparate phenomena—be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions—exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal node fitness (surface) factor and a latent Euclidean space-embedded node similarity (depth) factor. Building on recurring trends in the literature, the theory asserts that links arise due to individualistic as well as dyadic information and that important dyadic information making up the so-called depth factor is obscured by this essentially non-dyadic information making up the surface factor. Modelling based on this theory considerably outperforms popular power-law fitness and hyperbolic geometry explanations across 110 networks. Importantly, the degree distributions of the model resemble power-laws at small densities and log-normal distributions at larger densities, posing a reconciliatory solution to the long-standing debate on the nature and existence of scale-free networks. Validating this theory, a surface factor inversion approach on an economic world city network and an fMRI connectome results in considerably more geometrically aligned nearest neighbour networks, as is hypothesised to be the case for the depth factor. This establishes new foundations from which to understand, analyse, deconstruct and interpret network phenomena.
format	article
author	Keith Malcolm Smith
author_facet	Keith Malcolm Smith
author_sort	Keith Malcolm Smith
title	Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space
title_short	Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space
title_full	Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space
title_fullStr	Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space
title_full_unstemmed	Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space
title_sort	explaining the emergence of complex networks through log-normal fitness in a euclidean node similarity space
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8806a638a86c4cd889e213b8d3f79438
work_keys_str_mv	AT keithmalcolmsmith explainingtheemergenceofcomplexnetworksthroughlognormalfitnessinaeuclideannodesimilarityspace
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Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space

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