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• W3028060897 abstract "Non-Markovian spontaneous recovery processes with a time delay (memory) are ubiquitous in the real world. How does the non-Markovian characteristic affect failure propagation in complex networks? We consider failures due to internal causes at the nodal level and external failures due to an adverse environment, and develop a pair approximation analysis taking into account the two-node correlation. In general, a high failure stationary state can arise, corresponding to large-scale failures that can significantly compromise the functioning of the network. We uncover a striking phenomenon: memory associated with nodal recovery can counter-intuitively make the network more resilient against large-scale failures. In natural systems, the intrinsic non-Markovian characteristic of nodal recovery may thus be one reason for their resilience. In engineering design, incorporating certain non-Markovian features into the network may be beneficial to equipping it with a strong resilient capability to resist catastrophic failures." @default.
• W3028060897 created "2020-05-29" @default.
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• W3028060897 date "2020-05-19" @default.
• W3028060897 modified "2023-10-15" @default.
• W3028060897 title "Non-Markovian recovery makes complex networks more resilient against large-scale failures" @default.
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• W3028060897 doi "https://doi.org/10.1038/s41467-020-15860-2" @default.
• W3028060897 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7237476" @default.
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