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.

翻译：网络鲁棒性的学习与分析（包括可控鲁棒性和连通鲁棒性）对于各类网络系统抵御攻击至关重要。传统上，网络鲁棒性通过攻击模拟来确定，这种方式非常耗时，甚至无法应用于大规模网络。网络鲁棒性学习致力于以高精度和高速度学习网络鲁棒性，通过替代模拟为分析网络鲁棒性提供了强大工具。本文提出了一种新颖、多功能且统一的基于图Transformer的鲁棒性学习方法（NRL-GT），该方法从多个方面（包括鲁棒性曲线学习、整体鲁棒性学习以及合成网络分类）完成了可控鲁棒性学习和连通鲁棒性学习的任务。大量实验表明：1）NRL-GT是一个针对可控鲁棒性和连通鲁棒性的统一学习框架，展现出强大的泛化能力，能够在训练集和测试集分布不同时确保高精度；2）与前沿方法相比，NRL-GT能够同时从多个方面进行网络鲁棒性学习，并在更短的时间内获得更优结果。NRL-GT还能处理不同规模的复杂网络，具有低学习误差和高效率；3）值得提及的是，NRL-GT的主干网络可作为可迁移的特征学习模块，适用于不同规模的复杂网络及不同的下游任务。