Knowledge-Based Prediction of Network Controllability Robustness
Knowledge-Based Prediction of Network Controllability Robustness
复制标题
基于知识的网络可控鲁棒性预测
DOI：
10.1109/tnnls.2021.3071367
复制
发表时间：
2021-04-15
影响因子：
10.4
通讯作者：
Chen, Guanrong
中科院分区：
文献类型：
--
作者：
Lou, Yang;He, Yaodong;Chen, Guanrong
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.
