Predicting Robustness Performance with Noises in Network Representation

Abstract:

The connectivity and controllability of complex networks play an important role in ensuring the proper functioning of network systems. Robustness of connectivity and controllability is the ability of a network to maintain its basic functions against various malicious attacks. Convolutional neural network (CNN)-based approaches provide an efficient framework to approximate the network robustness, which significantly reduces computation time compared to attack simulations. In this paper, the performance of CNN-based prediction for connectivity and controllability robustness is investigated, when there are noises in the predicted network representation. Two CNN-based predictors are compared, 1) convolutional neural network-based robustness predictor (CNN-RP), and 2) spatial pyramid poolingbased convolutional neural network (CNN-SPP). Two aspects of network information noises are considered and investigated, 1) the random node information noises (RNIN), and 2) the random edge information noises (REIN). The following main conclusions are obtained from extensive experimental studies on synthetic networks: 1) CNN-RP is more tolerant than CNN-SPP to network noises, 2) The characteristics of small-world and scale-free networks make them have a favorable anti-noise ability, and 3) RNIN has less impact on CNN-based prediction performance than REIN, RNIN and REIN show opposite effects on prediction performance when the size of the predicted network is out of the training network size range.

Citation:

Chengpei Wu, Siyi Xu, and Junli Li “Predicting Robustness Performance with Noises in Network Representation” IEEE International Conference on Systems, Man, and Cybernetics (SMC) October 1-4, 2023, Maui, Hawaii, USA.

Download:

• To be updated.