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Strong connectivity in real directed networks

In many real, directed networks, the strongly connected component of nodes which are mutually reachable is very small. This does not fit with current theory, based on random graphs, according to which strong connectivity depends on mean degree and degree–degree correlations. And it has important imp...

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Detalles Bibliográficos
Autores principales: Rodgers, Niall, Tiňo, Peter, Johnson, Samuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10041124/
https://www.ncbi.nlm.nih.gov/pubmed/36927153
http://dx.doi.org/10.1073/pnas.2215752120
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author Rodgers, Niall
Tiňo, Peter
Johnson, Samuel
author_facet Rodgers, Niall
Tiňo, Peter
Johnson, Samuel
author_sort Rodgers, Niall
collection PubMed
description In many real, directed networks, the strongly connected component of nodes which are mutually reachable is very small. This does not fit with current theory, based on random graphs, according to which strong connectivity depends on mean degree and degree–degree correlations. And it has important implications for other properties of real networks and the dynamical behavior of many complex systems. We find that strong connectivity depends crucially on the extent to which the network has an overall direction or hierarchical ordering—a property measured by trophic coherence. Using percolation theory, we find the critical point separating weakly and strongly connected regimes and confirm our results on many real-world networks, including ecological, neural, trade, and social networks. We show that the connectivity structure can be disrupted with minimal effort by a targeted attack on edges which run counter to the overall direction. This means that many dynamical processes on networks can depend significantly on a small fraction of edges.
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spelling pubmed-100411242023-03-28 Strong connectivity in real directed networks Rodgers, Niall Tiňo, Peter Johnson, Samuel Proc Natl Acad Sci U S A Physical Sciences In many real, directed networks, the strongly connected component of nodes which are mutually reachable is very small. This does not fit with current theory, based on random graphs, according to which strong connectivity depends on mean degree and degree–degree correlations. And it has important implications for other properties of real networks and the dynamical behavior of many complex systems. We find that strong connectivity depends crucially on the extent to which the network has an overall direction or hierarchical ordering—a property measured by trophic coherence. Using percolation theory, we find the critical point separating weakly and strongly connected regimes and confirm our results on many real-world networks, including ecological, neural, trade, and social networks. We show that the connectivity structure can be disrupted with minimal effort by a targeted attack on edges which run counter to the overall direction. This means that many dynamical processes on networks can depend significantly on a small fraction of edges. National Academy of Sciences 2023-03-16 2023-03-21 /pmc/articles/PMC10041124/ /pubmed/36927153 http://dx.doi.org/10.1073/pnas.2215752120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Physical Sciences
Rodgers, Niall
Tiňo, Peter
Johnson, Samuel
Strong connectivity in real directed networks
title Strong connectivity in real directed networks
title_full Strong connectivity in real directed networks
title_fullStr Strong connectivity in real directed networks
title_full_unstemmed Strong connectivity in real directed networks
title_short Strong connectivity in real directed networks
title_sort strong connectivity in real directed networks
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10041124/
https://www.ncbi.nlm.nih.gov/pubmed/36927153
http://dx.doi.org/10.1073/pnas.2215752120
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