Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks

arXiv:2404.17947v1 Announce Type: cross
Abstract: Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to adversarial attacks. In this work, we theoretically define the concept of expected robustness in the context of attributed graphs and relate it to the classical definition of adversarial robustness in the graph representation learning literature. Our definition allows us to derive an upper bound of the expected robustness of Graph Convolutional Networks (GCNs) and Graph …

arxiv attacks cs.ai cs.cr cs.lg feature graph networks neural networks node robustness