IDEAS home Printed from https://ideas.repec.org/a/wsi/acsxxx/v27y2024i07n08ns021952592550002x.html
   My bibliography  Save this article

A Graph Convolutional Network Approach For Predicting Network Robustness

Author

Listed:
  • XINBIAO LU

    (College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China)

  • ZECHENG LIU

    (College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China)

  • HAO XING

    (College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China)

  • XUPENG XIE

    (College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China)

  • CHUNLIN YE

    (College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China)

Abstract

Network robustness, which includes controllability robustness and connectivity robustness, reflects the ability of a network system to withstand attacks. In this paper, a Graph Convolutional Network (GCN) approach is proposed for predicting network robustness. In contrast to the existing Convolutional Neural Network (CNN) approach, the network topology and the node characteristics are directly used as GCN input without being converted into a grayscale image. Due to the reduction in the number of feature maps, the model size of a GCN is greatly reduced to only 1%Â of a CNN. Extensive experimental studies on four representative networks and six real networks have proven that the proposed approach can achieve better predictive performance with less training and running time.

Suggested Citation

  • Xinbiao Lu & Zecheng Liu & Hao Xing & Xupeng Xie & Chunlin Ye, 2024. "A Graph Convolutional Network Approach For Predicting Network Robustness," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 27(07n08), pages 1-18, December.
    • Handle: RePEc:wsi:acsxxx:v:27:y:2024:i:07n08:n:s021952592550002x
    DOI: 10.1142/S021952592550002X
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S021952592550002X
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S021952592550002X?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.

    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:wsi:acsxxx:v:27:y:2024:i:07n08:n:s021952592550002x. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/acs/acs.shtml .

    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.