Non-Markovian recovery makes complex networks more resilient against large-scale failures

Zhao-Hua Lin, Mi Feng, Ming Tang (), Zonghua Liu (), Chen Xu, Pak Ming Hui and Ying-Cheng Lai
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Zhao-Hua Lin: East China Normal University
Mi Feng: East China Normal University
Ming Tang: East China Normal University
Zonghua Liu: East China Normal University
Chen Xu: Soochow University
Pak Ming Hui: The Chinese University of Hong Kong
Ying-Cheng Lai: Arizona State University

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-15860-2

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