no code implementations • 25 Aug 2022 • Chengpei Wu, Yang Lou, Ruizi Wu, Wenwen Liu, Junli Li
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.
no code implementations • 20 Mar 2022 • Yang Lou, Ruizi Wu, Junli Li, Lin Wang, Xiang Li, Guanrong Chen
Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.
