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A stochastic generative model of the World Trade Network

The World Trade Network (WTN) is a network of exchange flows among countries whose topological and statistical properties are a valuable source of information. Degree and strength (weighted degree) are key magnitudes to understand its structure and generative mechanisms. In this work, we describe a...

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Autores principales: García-Algarra, Javier, Mouronte-López, Mary Luz, Galeano, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6897986/
https://www.ncbi.nlm.nih.gov/pubmed/31811254
http://dx.doi.org/10.1038/s41598-019-54979-1
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author García-Algarra, Javier
Mouronte-López, Mary Luz
Galeano, Javier
author_facet García-Algarra, Javier
Mouronte-López, Mary Luz
Galeano, Javier
author_sort García-Algarra, Javier
collection PubMed
description The World Trade Network (WTN) is a network of exchange flows among countries whose topological and statistical properties are a valuable source of information. Degree and strength (weighted degree) are key magnitudes to understand its structure and generative mechanisms. In this work, we describe a stochastic model that yields synthetic networks that closely mimic the properties of annual empirical data. The model combines two popular mechanisms of network generation: preferential attachment and multiplicative process. Agreement between empirical and synthetic networks is checked using the available series from 1962 to 2017.
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spelling pubmed-68979862019-12-12 A stochastic generative model of the World Trade Network García-Algarra, Javier Mouronte-López, Mary Luz Galeano, Javier Sci Rep Article The World Trade Network (WTN) is a network of exchange flows among countries whose topological and statistical properties are a valuable source of information. Degree and strength (weighted degree) are key magnitudes to understand its structure and generative mechanisms. In this work, we describe a stochastic model that yields synthetic networks that closely mimic the properties of annual empirical data. The model combines two popular mechanisms of network generation: preferential attachment and multiplicative process. Agreement between empirical and synthetic networks is checked using the available series from 1962 to 2017. Nature Publishing Group UK 2019-12-06 /pmc/articles/PMC6897986/ /pubmed/31811254 http://dx.doi.org/10.1038/s41598-019-54979-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
García-Algarra, Javier
Mouronte-López, Mary Luz
Galeano, Javier
A stochastic generative model of the World Trade Network
title A stochastic generative model of the World Trade Network
title_full A stochastic generative model of the World Trade Network
title_fullStr A stochastic generative model of the World Trade Network
title_full_unstemmed A stochastic generative model of the World Trade Network
title_short A stochastic generative model of the World Trade Network
title_sort stochastic generative model of the world trade network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6897986/
https://www.ncbi.nlm.nih.gov/pubmed/31811254
http://dx.doi.org/10.1038/s41598-019-54979-1
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