  Learning and analysis of network robustness, including controllability
robustness and connectivity robustness, is critical for various networked
systems against attacks. Traditionally, network robustness is determined by
attack simulations, which is very time-consuming and even incapable for
large-scale networks. Network Robustness Learning, which is dedicated to
learning network robustness with high precision and high speed, provides a
powerful tool to analyze network robustness by replacing simulations. In this
paper, a novel versatile and unified robustness learning approach via graph
transformer (NRL-GT) is proposed, which accomplishes the task of
controllability robustness learning and connectivity robustness learning from
multiple aspects including robustness curve learning, overall robustness
learning, and synthetic network classification. Numerous experiments show that:
1) NRL-GT is a unified learning framework for controllability robustness and
connectivity robustness, demonstrating a strong generalization ability to
ensure high precision when training and test sets are distributed differently;
2) Compared to the cutting-edge methods, NRL-GT can simultaneously perform
network robustness learning from multiple aspects and obtains superior results
in less time. NRL-GT is also able to deal with complex networks of different
size with low learning error and high efficiency; 3) It is worth mentioning
that the backbone of NRL-GT can serve as a transferable feature learning module
for complex networks of different size and different downstream tasks.

Machine Learning;Artificial Intelligence;Cryptography and Security