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Knowledge-Based Prediction of Network Controllability Robustness
Network controllability robustness 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 controllability robustness is studied only for directed
networks and is determined by attack simulations, which is computationally time
consuming or even infeasible. In the present paper, an improved method for
predicting the controllability robustness of undirected networks 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.Comment: 11 pages, 8 figures in Paper; 33 pages, 2 figures in Supplementary
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