Transport collapse in dynamically evolving networks

Transport in complex networks can describe a variety of natural and human-engineered processes including biological, societal and technological ones. However, how the properties of the source and drain nodes can affect transport subject to random failures, attacks or maintenance optimization in the...

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Veröffentlicht in:Journal of the Royal Society interface 2023-03, Vol.20 (200), p.20220906-20220906
Hauptverfasser: Berthelot, Geoffroy, Tupikina, Liubov, Kang, Min-Yeong, Dedecker, Jérôme, Grebenkov, Denis
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container_issue 200
container_start_page 20220906
container_title Journal of the Royal Society interface
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creator Berthelot, Geoffroy
Tupikina, Liubov
Kang, Min-Yeong
Dedecker, Jérôme
Grebenkov, Denis
description Transport in complex networks can describe a variety of natural and human-engineered processes including biological, societal and technological ones. However, how the properties of the source and drain nodes can affect transport subject to random failures, attacks or maintenance optimization in the network remain unknown. In this article, the effects of both the distance between the source and drain nodes and the degree of the source node on the time of transport collapse are studied in scale-free and lattice-based transport networks. These effects are numerically evaluated for two strategies, which employ either transport-based or random link removal. Scale-free networks with small distances are found to result in larger times of collapse. In lattice-based networks, both the dimension and boundary conditions are shown to have a major effect on the time of collapse. We also show that adding a direct link between the source and the drain increases the robustness of scale-free networks when subject to random link removals. Interestingly, the distribution of the times of collapse is then similar to the one of lattice-based networks.
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subjects Condensed Matter
Life Sciences–Physics interface
Nonlinear Sciences
Physics
title Transport collapse in dynamically evolving networks
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