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Recovery coupling in multilayer networks

Author

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  • Michael M. Danziger

    (Northeastern University)

  • Albert-László Barabási

    (Northeastern University
    Harvard Medical School
    Central European University)

Abstract

The increased complexity of infrastructure systems has resulted in critical interdependencies between multiple networks—communication systems require electricity, while the normal functioning of the power grid relies on communication systems. These interdependencies have inspired an extensive literature on coupled multilayer networks, assuming a hard interdependence, where a component failure in one network causes failures in the other network, resulting in a cascade of failures across multiple systems. While empirical evidence of such hard failures is limited, the repair and recovery of a network requires resources typically supplied by other networks, resulting in documented interdependencies induced by the recovery process. In this work, we explore recovery coupling, capturing the dependence of the recovery of one system on the instantaneous functional state of another system. If the support networks are not functional, recovery will be slowed. Here we collected data on the recovery time of millions of power grid failures, finding evidence of universal nonlinear behavior in recovery following large perturbations. We develop a theoretical framework to address recovery coupling, predicting quantitative signatures different from the multilayer cascading failures. We then rely on controlled natural experiments to separate the role of recovery coupling from other effects like resource limitations, offering direct evidence of how recovery coupling affects a system’s functionality.

Suggested Citation

  • Michael M. Danziger & Albert-László Barabási, 2022. "Recovery coupling in multilayer networks," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28379-5
    DOI: 10.1038/s41467-022-28379-5
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    References listed on IDEAS

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    1. Antonio Majdandzic & Lidia A. Braunstein & Chester Curme & Irena Vodenska & Sary Levy-Carciente & H. Eugene Stanley & Shlomo Havlin, 2016. "Multiple tipping points and optimal repairing in interacting networks," Nature Communications, Nature, vol. 7(1), pages 1-10, April.
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    3. Almoghathawi, Yasser & Barker, Kash & Albert, Laura A., 2019. "Resilience-driven restoration model for interdependent infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 12-23.
    4. Monsalve, Mauricio & de la Llera, Juan Carlos, 2019. "Data-driven estimation of interdependencies and restoration of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 167-180.
    5. Ivana Bachmann & Javier Bustos-Jiménez & Benjamin Bustos, 2020. "A Survey on Frameworks Used for Robustness Analysis on Interdependent Networks," Complexity, Hindawi, vol. 2020, pages 1-17, April.
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    Cited by:

    1. Liang, Yuan & Qi, Mingze & Huangpeng, Qizi & Duan, Xiaojun, 2023. "Percolation of interlayer feature-correlated multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. 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.
    3. Opabola, Eyitayo A. & Galasso, Carmine, 2024. "A probabilistic framework for post-disaster recovery modeling of buildings and electric power networks in developing countries," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Hirwa, Jusse & Zolan, Alexander & Becker, William & Flamand, Tülay & Newman, Alexandra, 2023. "Optimizing design and dispatch of a resilient renewable energy microgrid for a South African hospital," Applied Energy, Elsevier, vol. 348(C).
    5. Blagojević, Nikola & Didier, Max & Stojadinović, Božidar, 2022. "Quantifying component importance for disaster resilience of communities with interdependent civil infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Yijun Liu & Xiaokun Jin & Yunrui Zhang, 2024. "Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-21, December.

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