2 research outputs found
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
Informatio
CNN-based Prediction of Network Robustness With Missing Edges
Connectivity and controllability of a complex network are two important
issues that guarantee a networked system to function. Robustness of
connectivity and controllability guarantees the system to function properly and
stably under various malicious attacks. Evaluating network robustness using
attack simulations is time consuming, while the convolutional neural network
(CNN)-based prediction approach provides a cost-efficient method to approximate
the network robustness. In this paper, we investigate the performance of
CNN-based approaches for connectivity and controllability robustness
prediction, when partial network information is missing, namely the adjacency
matrix is incomplete. Extensive experimental studies are carried out. A
threshold is explored that if a total amount of more than 7.29\% information is
lost, the performance of CNN-based prediction will be significantly degenerated
for all cases in the experiments. Two scenarios of missing edge representations
are compared, 1) a missing edge is marked `no edge' in the input for
prediction, and 2) a missing edge is denoted using a special marker of
`unknown'. Experimental results reveal that the first representation is
misleading to the CNN-based predictors.Comment: In Proceedings of the IEEE 2022 International Joint Conference on
Neural Networks (IJCNN