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Reinforcement Learning-Based Network Dismantling by Targeting Maximum-Degree Nodes in the Giant Connected Component

Author

Listed:
  • Shixuan Liu

    (Laboratory for Big Data and Decision, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
    These authors contributed equally to this work.)

  • Tianle Pu

    (Laboratory for Big Data and Decision, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
    These authors contributed equally to this work.)

  • Li Zeng

    (Laboratory for Big Data and Decision, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
    These authors contributed equally to this work.)

  • Yunfei Wang

    (National Key Laboratory of Information Systems Engineering, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Haoxiang Cheng

    (Laboratory for Big Data and Decision, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Zhong Liu

    (Laboratory for Big Data and Decision, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or the average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to effectively addressing the network dismantling problem.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2766-:d:1473097
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    References listed on IDEAS

    as
    1. 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).
    2. Alessandro Vespignani, 2018. "Twenty years of network science," Nature, Nature, vol. 558(7711), pages 528-529, June.
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