Predicting the Robustness of Undirected Network Controllability

Yang Lou, Yaodong He, Lin Wang, Kim Fung Tsang, Guanrong Chen

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

Robustness of the network controllability reflects how well a networked system can maintain its controllability against destructive attacks. The measure of the network controllability robustness 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 determined by attack simulations, which is computationally time consuming. In this paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a convolutional neural network. This approach is motivated by the following observations: 1) there is no clear correlation between the topological features and the controllability robustness of a general undirected network, 2) the adjacency matrix of a network can be represented as a gray-scale image, 3) the convolutional neural network technique has proved successful in image processing without human intervention. In the new framework, preprocessing and filtering are embedded, and a sufficiently large number of training datasets generated by simulations are used to train several convolutional neural networks for classification and prediction, respectively. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting the controllability robustness of undirected networks is more accurate and reliable than the conventional single convolutional neural network predictor.

原文English
主出版物標題Proceedings of the 39th Chinese Control Conference, CCC 2020
編輯Jun Fu, Jian Sun
發行者IEEE Computer Society
頁面4550-4553
頁數4
ISBN(電子)9789881563903
DOIs
出版狀態Published - 7月 2020
事件39th Chinese Control Conference, CCC 2020 - Shenyang, China
持續時間: 27 7月 202029 7月 2020

出版系列

名字Chinese Control Conference, CCC
2020-July
ISSN(列印)1934-1768
ISSN(電子)2161-2927

Conference

Conference39th Chinese Control Conference, CCC 2020
國家/地區China
城市Shenyang
期間27/07/2029/07/20

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