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Vulnerability of complex networks under path-based attacks

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  • Pu, Cun-Lai
  • Cui, Wei

Abstract

We investigate vulnerability of complex networks including model networks and real-world networks subject to path-based attacks. Specifically, we remove approximately the longest simple path from a network iteratively until there are no paths left in the network. We propose two algorithms, the random augmenting approach (RPA) and the Hamilton-path based approach (HPA), for finding the approximately longest simple path in a network. Results demonstrate that steps of longest-path attacks increase with network density linearly for random networks, while exponentially increasing for scale-free networks. The more homogeneous the degree distribution is, the more fragile the network, which is different from the previous results of node or edge attacks. HPA is generally more efficient than RPA in the longest-path attacks of complex networks. These findings further help us understand the vulnerability of complex systems, better protect complex systems, and design more tolerant complex systems.

Suggested Citation

  • Pu, Cun-Lai & Cui, Wei, 2015. "Vulnerability of complex networks under path-based attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 622-629.
    • Handle: RePEc:eee:phsmap:v:419:y:2015:i:c:p:622-629
    DOI: 10.1016/j.physa.2014.10.038
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    References listed on IDEAS

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    1. Karimi, Fariba & Holme, Petter, 2013. "Threshold model of cascades in empirical temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3476-3483.
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    Cited by:

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    2. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    3. Alexander Shiroky & Andrey Kalashnikov, 2023. "Influence of the Internal Structure on the Integral Risk of a Complex System on the Example of the Risk Minimization Problem in a “Star” Type Structure," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    4. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    5. Wang, Jianwei & Wang, Siyuan & Wang, Ziwei, 2022. "Robustness of spontaneous cascading dynamics driven by reachable area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    6. Kashin Sugishita & Yasuo Asakura, 2021. "Vulnerability studies in the fields of transportation and complex networks: a citation network analysis," Public Transport, Springer, vol. 13(1), pages 1-34, March.
    7. Zhou, Hongli & Zhang, Xiaodong & Hu, Yang, 2020. "Robustness of open source product innovation community’s knowledge collaboration network under the dynamic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    8. Fei Ma & Fei Liu & Kum Fai Yuen & Polin Lai & Qipeng Sun & Xiaodan Li, 2019. "Cascading Failures and Vulnerability Evolution in Bus–Metro Complex Bilayer Networks under Rainstorm Weather Conditions," IJERPH, MDPI, vol. 16(3), pages 1-30, January.
    9. Lekha, Divya Sindhu & Balakrishnan, Kannan, 2020. "Central attacks in complex networks: A revisit with new fallback strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    10. Zhou, Hong-Li & Zhang, Xiao-Dong, 2018. "Dynamic robustness of knowledge collaboration network of open source product development community," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 601-612.
    11. Viljoen, Nadia M. & Joubert, Johan W., 2016. "The vulnerability of the global container shipping network to targeted link disruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 396-409.

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