Knowledge-Based Prediction of Network Controllability Robustness

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

7 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)5739-5750
Number of pages12
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume33
Issue number10
Online published16 Apr 2021
Publication statusPublished - Oct 2022

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.

Research Area(s)

  • Complex network, Controllability, convolutional neural network (CNN), Correlation, Image edge detection, Knowledge based systems, knowledge-based prediction, Neural networks, Optimization, Robustness