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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
Descripción
Sumario: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.