TY - JOUR
T1 - Non-Markovian recovery makes complex networks more resilient against large-scale failures
AU - Lin, Zhao-Hua
AU - Feng, Mi
AU - Tang, Ming
AU - Liu, Zonghua
AU - Xu, Chen
AU - Hui, Pak Ming
AU - Lai, Ying-Cheng
N1 - This work was supported by the National Natural Science Foundation of China (Grant Nos. 11975099,
11575041, 11675056 and 11835003), the Natural Science Foundation of Shanghai (Grant No. 18ZR1412200), and the Science and Technology Commission of Shanghai Municipality (Grant No. 14DZ2260800). Y.C.L. would like to acknowledge support from the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office
of Naval Research through Grant No. N00014-16-1-2828.
PY - 2020/5/19
Y1 - 2020/5/19
N2 - 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.
AB - 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.
UR - https://www.nature.com/articles/s41467-020-15860-2
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85084964527&origin=inward
U2 - 10.1038/s41467-020-15860-2
DO - 10.1038/s41467-020-15860-2
M3 - Journal article
SN - 2041-1723
VL - 11
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 2490
ER -