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From random failures to targeted attacks in network dismantling

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  • Wandelt, Sebastian
  • Lin, Wei
  • Sun, Xiaoqian
  • Zanin, Massimiliano

Abstract

It is well-known that real-world systems, modeled as complex networks, are mostly robust against random failures but susceptible to targeted attacks. In this study, we propose a novel perspective to solve the network dismantling problem. Instead of designing an effective attack from scratch, we show how knowledge extracted from random failures in the network leads to extremely effective attacks. This observed connection between random failures and targeted attacks is striking on its own. Experiments on a wide range of networks show the efficacy of our novel method for network dismantling, providing an excellent trade-off between attack quality and scalability. We believe that our contribution also stimulates research in related domains, including social network influence analysis, spreading dynamics in networks, and efficiency considerations.

Suggested Citation

  • Wandelt, Sebastian & Lin, Wei & Sun, Xiaoqian & Zanin, Massimiliano, 2022. "From random failures to targeted attacks in network dismantling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pa:s0951832021006335
    DOI: 10.1016/j.ress.2021.108146
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    4. Shixuan Liu & Tianle Pu & Li Zeng & Yunfei Wang & Haoxiang Cheng & Zhong Liu, 2024. "Reinforcement Learning-Based Network Dismantling by Targeting Maximum-Degree Nodes in the Giant Connected Component," Mathematics, MDPI, vol. 12(17), pages 1-15, September.
    5. Wandelt, Sebastian & Sun, Xiaoqian & Zhang, Anming, 2023. "Towards analyzing the robustness of the Integrated Global Transportation Network Abstraction (IGTNA)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
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    7. Li, Jiahui & Qi, Xiaogang & He, Yi & Liu, Lifang, 2024. "SDN candidate and protection path selection for link failure protection in hybrid SDNs," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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