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Cascading failures on interdependent networks with star dependent links

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  • Zhang, Tianqiao
  • Zhang, Yang
  • Zhu, Xuzhen
  • Chen, Junliang

Abstract

In this paper, we propose a cascading failure model with star interdependent links. To describe the model theoretically, a generalized percolation theory is developed. Through extensive numerical simulations and theoretical analyses, we study the phase transition of the system on artificial networks. On both ER-ER and SF-SF networks, the system exhibits a continuous (discontinuous) phase transition for small (large) fraction of service nodes. On ER-ER networks, the more service links of a service node, the system is more likely to exhibit a discontinuous phase transition. On SF-SF networks, the service links of a service node cannot alter the phase of the system. Our suggested theory agrees well with the numerical simulations.

Suggested Citation

  • Zhang, Tianqiao & Zhang, Yang & Zhu, Xuzhen & Chen, Junliang, 2019. "Cascading failures on interdependent networks with star dependent links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119312889
    DOI: 10.1016/j.physa.2019.122222
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    References listed on IDEAS

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    1. Wang, Wei & Cai, Meng & Zheng, Muhua, 2018. "Social contagions on correlated multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 121-128.
    2. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo & Rapisarda, Andrea, 2004. "Error and attack tolerance of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(1), pages 388-394.
    3. La Rocca, Cristian E. & Stanley, H. Eugene & Braunstein, Lidia A., 2018. "Strategy for stopping failure cascades in interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 577-583.
    4. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    5. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    6. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
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    Cited by:

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    2. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).

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