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Node-Level Resilience Loss in Dynamic Complex Networks

In an increasingly connected world, the resilience of networked dynamical systems is important in the fields of ecology, economics, critical infrastructures, and organizational behaviour. Whilst we understand small-scale resilience well, our understanding of large-scale networked resilience is limit...

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Autores principales: Moutsinas, Giannis, Guo, Weisi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7046645/
https://www.ncbi.nlm.nih.gov/pubmed/32109933
http://dx.doi.org/10.1038/s41598-020-60501-9
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author Moutsinas, Giannis
Guo, Weisi
author_facet Moutsinas, Giannis
Guo, Weisi
author_sort Moutsinas, Giannis
collection PubMed
description In an increasingly connected world, the resilience of networked dynamical systems is important in the fields of ecology, economics, critical infrastructures, and organizational behaviour. Whilst we understand small-scale resilience well, our understanding of large-scale networked resilience is limited. Recent research in predicting the effective network-level resilience pattern has advanced our understanding of the coupling relationship between topology and dynamics. However, a method to estimate the resilience of an individual node within an arbitrarily large complex network governed by non-linear dynamics is still lacking. Here, we develop a sequential mean-field approach and show that after 1-3 steps of estimation, the node-level resilience function can be represented with up to 98% accuracy. This new understanding compresses the higher dimensional relationship into a one-dimensional dynamic for tractable understanding, mapping the relationship between local dynamics and the statistical properties of network topology. By applying this framework to case studies in ecology and biology, we are able to not only understand the general resilience pattern of the network, but also identify the nodes at the greatest risk of failure and predict the impact of perturbations. These findings not only shed new light on the causes of resilience loss from cascade effects in networked systems, but the identification capability could also be used to prioritize protection, quantify risk, and inform the design of new system architectures.
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spelling pubmed-70466452020-03-04 Node-Level Resilience Loss in Dynamic Complex Networks Moutsinas, Giannis Guo, Weisi Sci Rep Article In an increasingly connected world, the resilience of networked dynamical systems is important in the fields of ecology, economics, critical infrastructures, and organizational behaviour. Whilst we understand small-scale resilience well, our understanding of large-scale networked resilience is limited. Recent research in predicting the effective network-level resilience pattern has advanced our understanding of the coupling relationship between topology and dynamics. However, a method to estimate the resilience of an individual node within an arbitrarily large complex network governed by non-linear dynamics is still lacking. Here, we develop a sequential mean-field approach and show that after 1-3 steps of estimation, the node-level resilience function can be represented with up to 98% accuracy. This new understanding compresses the higher dimensional relationship into a one-dimensional dynamic for tractable understanding, mapping the relationship between local dynamics and the statistical properties of network topology. By applying this framework to case studies in ecology and biology, we are able to not only understand the general resilience pattern of the network, but also identify the nodes at the greatest risk of failure and predict the impact of perturbations. These findings not only shed new light on the causes of resilience loss from cascade effects in networked systems, but the identification capability could also be used to prioritize protection, quantify risk, and inform the design of new system architectures. Nature Publishing Group UK 2020-02-27 /pmc/articles/PMC7046645/ /pubmed/32109933 http://dx.doi.org/10.1038/s41598-020-60501-9 Text en © The Author(s) 2020 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
Moutsinas, Giannis
Guo, Weisi
Node-Level Resilience Loss in Dynamic Complex Networks
title Node-Level Resilience Loss in Dynamic Complex Networks
title_full Node-Level Resilience Loss in Dynamic Complex Networks
title_fullStr Node-Level Resilience Loss in Dynamic Complex Networks
title_full_unstemmed Node-Level Resilience Loss in Dynamic Complex Networks
title_short Node-Level Resilience Loss in Dynamic Complex Networks
title_sort node-level resilience loss in dynamic complex networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7046645/
https://www.ncbi.nlm.nih.gov/pubmed/32109933
http://dx.doi.org/10.1038/s41598-020-60501-9
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