A Learning Convolutional Neural Network Approach for Network Robustness Prediction

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Scopus Citations
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Author(s)

  • Ruizi Wu
  • Junli Li
  • Lin Wang
  • Xiang Li

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)4531-4544
Journal / PublicationIEEE Transactions on Cybernetics
Volume53
Issue number7
Online published10 Oct 2022
Publication statusPublished - Jul 2023

Abstract

Network robustness is critical for various societal and industrial networks against malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node-or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible for large-scale networks. In this article, an improved method for network robustness prediction is developed based on learning feature representation using the convolutional neural network (LFR-CNN). In this scheme, the higher-dimensional network data are compressed into lower-dimensional representations, which are then passed to a convolutional neural network to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that: 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly smaller prediction errors; 2) LFR-CNN is insensitive to the variation of the input network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; and 4) LFR-CNN not only accurately predicts the network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.

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Research Area(s)

  • Complex network, Complex networks, Controllability, convolutional neural network (CNN), Convolutional neural networks, graph representation learning, Indexes, Neural networks, Optimization, prediction, robustness, Robustness

Citation Format(s)

A Learning Convolutional Neural Network Approach for Network Robustness Prediction. / Lou, Yang; Wu, Ruizi; Li, Junli et al.
In: IEEE Transactions on Cybernetics, Vol. 53, No. 7, 07.2023, p. 4531-4544.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review