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Optimal attack strategy of complex networks based on tabu search

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  • Deng, Ye
  • Wu, Jun
  • Tan, Yue-jin

Abstract

The problem of network disintegration has broad applications and recently has received growing attention, such as network confrontation and disintegration of harmful networks. This paper presents an optimized attack strategy model for complex networks and introduces the tabu search into the network disintegration problem to identify the optimal attack strategy, which is a heuristic optimization algorithm and rarely applied to the study of network robustness. The efficiency of the proposed solution was verified by comparing it with other attack strategies used in various model networks and real-world network. Numerical experiments suggest that our solution can improve the effect of network disintegration and that the “best” choice for node failure attacks can be identified through global searches. Our understanding of the optimal attack strategy may also shed light on a new property of the nodes within network disintegration and deserves additional study.

Suggested Citation

  • Deng, Ye & Wu, Jun & Tan, Yue-jin, 2016. "Optimal attack strategy of complex networks based on tabu search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 74-81.
    • Handle: RePEc:eee:phsmap:v:442:y:2016:i:c:p:74-81
    DOI: 10.1016/j.physa.2015.08.043
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    References listed on IDEAS

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    Cited by:

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    2. Lordan, Oriol & Albareda-Sambola, Maria, 2019. "Exact calculation of network robustness," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 276-280.
    3. Han, Jihui & Zhang, Ge & Dong, Gaogao & Zhao, Longfeng & Shi, Yuefeng & Zou, Yijiang, 2024. "Exact analysis of generalized degree-based percolation without memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
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    6. Tianle Pu & Li Zeng & Chao Chen, 2024. "Deep Reinforcement Learning for Network Dismantling: A K-Core Based Approach," Mathematics, MDPI, vol. 12(8), pages 1-12, April.

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