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A novel dimension reduction method with information entropy to evaluate network resilience

Author

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  • Wu, Chengxing
  • Duan, Dongli
  • Xiao, Renbin

Abstract

Network resilience is a fundamental property of many high-dimensional systems to maintain their functions in the event of disturbances and errors. Despite substantial progress, evaluative tools for network resilience are still relatively limited. Here, we develop a new framework based on the information entropy of nodes to evaluate network resilience. The proposed framework allows us to capture the global state of high-dimensional systems through information entropy weighting and derive an efficient one-dimensional (1D) resilience function. Using computer-generated networks and 72 real-world data of various complex systems, we verify the feasibility of the analysis framework based on information entropy. The results show that the proposed framework can effectively capture the resilience state of these networks and achieve higher accuracy than previous methods in real-world networks. Furthermore, we find that the accuracy of 1D reduction depends on the weight distribution of the network structure and choosing appropriate weights helps to improve the accuracy of dimension reduction.

Suggested Citation

  • Wu, Chengxing & Duan, Dongli & Xiao, Renbin, 2023. "A novel dimension reduction method with information entropy to evaluate network resilience," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 620(C).
    • Handle: RePEc:eee:phsmap:v:620:y:2023:i:c:s0378437123002820
    DOI: 10.1016/j.physa.2023.128727
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    Citations

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    Cited by:

    1. Tu, Chengyi & Luo, Jianhong & Fan, Ying & Pan, Xuwei, 2023. "Dimensionality reduction in stochastic complex dynamical networks," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Alexander Stolz & Jet Hoe Tang & Stefan G. Fischer & Kai Fischer, 2024. "A rheological model analog for assessing the resilience of socio-technical systems across sectors," Environment Systems and Decisions, Springer, vol. 44(4), pages 922-945, December.
    3. Wu, Chengxing & Duan, Dongli, 2024. "Collapse process prediction of mutualistic dynamical networks with k-core and dimension reduction method," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    4. Yuan, Kai & Sui, Xi & Zhang, Shijie & Xiao, Ning-cong & Hu, Jinghan, 2024. "AK-SYS-IE: A novel adaptive Kriging-based method for system reliability assessment combining information entropy," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

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