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Interdependent networks: vulnerability analysis and strategies to limit cascading failure

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  • Gaihua Fu
  • Richard Dawson
  • Mehdi Khoury
  • Seth Bullock

Abstract

Network theory is increasingly employed to study the structure and behaviour of social, physical and technological systems — including civil infrastructure. Many of these systems are interconnected and the interdependencies between them allow disruptive events to propagate across networks, enabling damage to spread far beyond the immediate footprint of disturbance. In this research we experiment with a model to characterise the configuration of interdependencies in terms of direction, redundancy, and extent, and we analyse the performance of interdependent systems with a wide range of possible coupling modes. We demonstrate that networks with directed dependencies are less robust than those with undirected dependencies, and that the degree of redundancy in inter-network dependencies can have a differential effect on robustness depending on the directionality of the dependencies. As interdependencies between many real-world systems exhibit these characteristics, it is likely that many such systems operate near their critical thresholds. The vulnerability of an interdependent network is shown to be reducible in a cost effective way, either by optimising inter-network connections, or by hardening high degree nodes. The results improve understanding of the influence of interdependencies on system performance and provide insight into how to mitigate associated risks. Copyright The Author(s) 2014

Suggested Citation

  • Gaihua Fu & Richard Dawson & Mehdi Khoury & Seth Bullock, 2014. "Interdependent networks: vulnerability analysis and strategies to limit cascading failure," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(7), pages 1-10, July.
  • Handle: RePEc:spr:eurphb:v:87:y:2014:i:7:p:1-10:10.1140/epjb/e2014-40876-y
    DOI: 10.1140/epjb/e2014-40876-y
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    Citations

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    Cited by:

    1. Johnson, Caroline A. & Flage, Roger & Guikema, Seth D., 2019. "Characterising the robustness of coupled power-law networks," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Li, Xin & Wu, Haotian & Scoglio, Caterina & Gruenbacher, Don, 2015. "Robust allocation of weighted dependency links in cyber–physical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 316-327.
    3. Bellè, Andrea & Abdin, Adam F. & Fang, Yi-Ping & Zeng, Zhiguo & Barros, Anne, 2023. "A resilience-based framework for the optimal coupling of interdependent critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Sybil Derrible, 2017. "Urban infrastructure is not a tree: Integrating and decentralizing urban infrastructure systems," Environment and Planning B, , vol. 44(3), pages 553-569, May.
    5. Duan, Dongli & Yan, Qi & Rong, Yisheng & Hou, Gege, 2022. "Predicting the cascading failure of dynamical networks based on a new dimension reduction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    6. Bellè, Andrea & Abdin, Adam F. & Fang, Yi-Ping & Zeng, Zhiguo & Barros, Anne, 2023. "A data-driven distributionally robust approach for the optimal coupling of interdependent critical infrastructures under random failures," European Journal of Operational Research, Elsevier, vol. 309(2), pages 872-889.
    7. Stødle, Kaia & Metcalfe, Caroline A. & Brunner, Logan G. & Saliani, Julian N. & Flage, Roger & Guikema, Seth D., 2021. "Dependent infrastructure system modeling: A case study of the St. Kitts power and water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

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    Keywords

    Statistical and Nonlinear Physics;

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