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Structural instability of large-scale functional networks

We study how large functional networks can grow stably under possible cascading overload failures and evaluated the maximum stable network size above which even a small-scale failure would cause a fatal breakdown of the network. Employing a model of cascading failures induced by temporally fluctuati...

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Detalles Bibliográficos
Autores principales: Mizutaka, Shogo, Yakubo, Kousuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5519067/
https://www.ncbi.nlm.nih.gov/pubmed/28727823
http://dx.doi.org/10.1371/journal.pone.0181247
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author Mizutaka, Shogo
Yakubo, Kousuke
author_facet Mizutaka, Shogo
Yakubo, Kousuke
author_sort Mizutaka, Shogo
collection PubMed
description We study how large functional networks can grow stably under possible cascading overload failures and evaluated the maximum stable network size above which even a small-scale failure would cause a fatal breakdown of the network. Employing a model of cascading failures induced by temporally fluctuating loads, the maximum stable size n(max) has been calculated as a function of the load reduction parameter r that characterizes how quickly the total load is reduced during the cascade. If we reduce the total load sufficiently fast (r ≥ r(c)), the network can grow infinitely. Otherwise, n(max) is finite and increases with r. For a fixed r(< r(c)), n(max) for a scale-free network is larger than that for an exponential network with the same average degree. We also discuss how one detects and avoids the crisis of a fatal breakdown of the network from the relation between the sizes of the initial network and the largest component after an ordinarily occurring cascading failure.
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spelling pubmed-55190672017-08-07 Structural instability of large-scale functional networks Mizutaka, Shogo Yakubo, Kousuke PLoS One Research Article We study how large functional networks can grow stably under possible cascading overload failures and evaluated the maximum stable network size above which even a small-scale failure would cause a fatal breakdown of the network. Employing a model of cascading failures induced by temporally fluctuating loads, the maximum stable size n(max) has been calculated as a function of the load reduction parameter r that characterizes how quickly the total load is reduced during the cascade. If we reduce the total load sufficiently fast (r ≥ r(c)), the network can grow infinitely. Otherwise, n(max) is finite and increases with r. For a fixed r(< r(c)), n(max) for a scale-free network is larger than that for an exponential network with the same average degree. We also discuss how one detects and avoids the crisis of a fatal breakdown of the network from the relation between the sizes of the initial network and the largest component after an ordinarily occurring cascading failure. Public Library of Science 2017-07-20 /pmc/articles/PMC5519067/ /pubmed/28727823 http://dx.doi.org/10.1371/journal.pone.0181247 Text en © 2017 Mizutaka, Yakubo http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mizutaka, Shogo
Yakubo, Kousuke
Structural instability of large-scale functional networks
title Structural instability of large-scale functional networks
title_full Structural instability of large-scale functional networks
title_fullStr Structural instability of large-scale functional networks
title_full_unstemmed Structural instability of large-scale functional networks
title_short Structural instability of large-scale functional networks
title_sort structural instability of large-scale functional networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5519067/
https://www.ncbi.nlm.nih.gov/pubmed/28727823
http://dx.doi.org/10.1371/journal.pone.0181247
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