Analysis and synthesis of a growing network model generating dense scale-free networks via category theory

Abstract We propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-f...

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Autores principales:	Taichi Haruna, Yukio-Pegio Gunji
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0bc10a92fd71473e9993bb9618b71509
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spelling	oai:doaj.org-article:0bc10a92fd71473e9993bb9618b715092021-12-02T11:57:56ZAnalysis and synthesis of a growing network model generating dense scale-free networks via category theory10.1038/s41598-020-79318-72045-2322https://doaj.org/article/0bc10a92fd71473e9993bb9618b715092020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79318-7https://doaj.org/toc/2045-2322Abstract We propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-free networks but with a different higher-order network structure. The modification is mediated by category theory. Category theory can identify a duality structure hidden in the previous model. The proposed model is built so that the identified duality is preserved. This work is a novel application of category theory for designing a network model focusing on a universal algebraic structure.Taichi HarunaYukio-Pegio GunjiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Taichi Haruna Yukio-Pegio Gunji Analysis and synthesis of a growing network model generating dense scale-free networks via category theory
description	Abstract We propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-free networks but with a different higher-order network structure. The modification is mediated by category theory. Category theory can identify a duality structure hidden in the previous model. The proposed model is built so that the identified duality is preserved. This work is a novel application of category theory for designing a network model focusing on a universal algebraic structure.
format	article
author	Taichi Haruna Yukio-Pegio Gunji
author_facet	Taichi Haruna Yukio-Pegio Gunji
author_sort	Taichi Haruna
title	Analysis and synthesis of a growing network model generating dense scale-free networks via category theory
title_short	Analysis and synthesis of a growing network model generating dense scale-free networks via category theory
title_full	Analysis and synthesis of a growing network model generating dense scale-free networks via category theory
title_fullStr	Analysis and synthesis of a growing network model generating dense scale-free networks via category theory
title_full_unstemmed	Analysis and synthesis of a growing network model generating dense scale-free networks via category theory
title_sort	analysis and synthesis of a growing network model generating dense scale-free networks via category theory
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/0bc10a92fd71473e9993bb9618b71509
work_keys_str_mv	AT taichiharuna analysisandsynthesisofagrowingnetworkmodelgeneratingdensescalefreenetworksviacategorytheory AT yukiopegiogunji analysisandsynthesisofagrowingnetworkmodelgeneratingdensescalefreenetworksviacategorytheory
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Analysis and synthesis of a growing network model generating dense scale-free networks via category theory

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