Knowledge-Based Prediction of Network Controllability Robustness

Yang LOU, Yaodong HE, Lin WANG, Kim Fung TSANG, Guanrong CHEN*

*Corresponding author for this work

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

Abstract

Network controllability robustness (CR) reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the CR is determined by attack simulations, which is computationally time-consuming or even infeasible. In this article, an improved method for predicting the network CR is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.

Original languageEnglish
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusE-pub ahead of print - 16 Apr 2021
Externally publishedYes

Bibliographical note

Funding information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62002249 and Grant 61873167 and in part by the Hong Kong ITF under Grant CityU ITP/058/17LP.

Publisher Copyright:
IEEE

Keywords

  • Complex network
  • Controllability
  • convolutional neural network (CNN)
  • knowledge-based prediction
  • robustness

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