IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v620y2023ics0378437123002820.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123002820
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.128727?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:620:y:2023:i:c:s0378437123002820. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.