CNN-based Prediction of Network Robustness With Missing Edges
This work addresses the challenge of efficiently evaluating network robustness in systems with incomplete data, though it is incremental as it builds on existing CNN-based prediction methods.
The paper tackles the problem of predicting network robustness for connectivity and controllability using CNN-based approaches when edges are missing, finding that performance significantly degrades if more than 7.29% of information is lost and that marking missing edges as 'no edge' misleads predictors.
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.