Enhancing CNN-Based Network Robustness Predictors Through Representation Recovery Against Information Noise

Connectivity robustness and controllability robustness play a crucial role in maintaining the stability of complex network systems. Recently, complex network systems have faced an increasing number of malicious attacks and random failures, emphasizing the vital importance of assessing their performance. The CNN-based predictor serves as a powerful tool for evaluating the robustness of complex networks. However, the excellent performance of CNN-based predictors requires complete network data, which is often not available in real-world networks. In this paper, we investigate the recovery of losing information in networks. The main contributions can be summarized as follows: 1) Explore the impact of information loss in complex networks on CNN-based robustness prediction models. 2) Propose three recovery algorithms for addressing information loss, effectively improving the issue of losing network information. Extensive experiments demonstrate that in the presence of information loss in complex networks, CNN-based predictors exhibit higher prediction errors. However, through the application of recovery algorithms to recover losing information, a significant reduction in prediction errors is achieved.