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

Author

Listed:
  • Zhao-Hua Lin

    (East China Normal University)

  • Mi Feng

    (East China Normal University)

  • Ming Tang

    (East China Normal University
    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)

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.

Suggested Citation

  • Zhao-Hua Lin & Mi Feng & Ming Tang & Zonghua Liu & Chen Xu & Pak Ming Hui & Ying-Cheng Lai, 2020. "Non-Markovian recovery makes complex networks more resilient against large-scale failures," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15860-2
    DOI: 10.1038/s41467-020-15860-2
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    Cited by:

    1. Longbing Cao & Chengzhang Zhu, 2022. "Personalized next-best action recommendation with multi-party interaction learning for automated decision-making," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-22, January.
    2. Qu, Junyi & Liu, Ying & Tang, Ming & Guan, Shuguang, 2022. "Identification of the most influential stocks in financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Hao Wu & Xiangyi Meng & Michael M. Danziger & Sean P. Cornelius & Hui Tian & Albert-László Barabási, 2022. "Fragmentation of outage clusters during the recovery of power distribution grids," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. Wen-Juan Xu & Chen-Yang Zhong & Fei Ren & Tian Qiu & Rong-Da Chen & Yun-Xin He & Li-Xin Zhong, 2020. "Evolutionary dynamics in financial markets with heterogeneities in strategies and risk tolerance," Papers 2010.08962, arXiv.org.
    5. Li, Jiachen & Li, Wenjie & Gao, Feng & Cai, Meng & Zhang, Zengping & Liu, Xiaoyang & Wang, Wei, 2024. "Social contagions on higher-order community networks," Applied Mathematics and Computation, Elsevier, vol. 478(C).
    6. Ruan, Zhongyuan & Zhang, Lina & Shu, Xincheng & Xuan, Qi, 2022. "Social contagion with negative feedbacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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