A Graph Transformer-Driven Approach for Network Robustness Learning

arXiv:2306.06913v2 Announce Type: replace
Abstract: 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 …

abstract analysis and analysis arxiv attacks connectivity cs.ai cs.cr cs.lg graph network networks robustness scale simulations systems transformer type