A Learning Convolutional Neural Network Approach for Network Robustness Prediction

Yang LOU, Ruizi WU, Junli LI, Lin WANG, Xiang LI, Guanrong CHEN

Research output: Journal PublicationsJournal Article (refereed)peer-review

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.
Original languageEnglish
Number of pages14
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusE-pub ahead of print - 10 Oct 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

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

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