  Existing studies show that node representations generated by graph neural
networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable
perturbations of adjacent matrix and node features. Thus, it is requisite to
learn robust representations in graph neural networks. To improve the
robustness of graph representation learning, we propose a novel Graph
Adversarial Contrastive Learning framework (GraphACL) by introducing
adversarial augmentations into graph self-supervised learning. In this
framework, we maximize the mutual information between local and global
representations of a perturbed graph and its adversarial augmentations, where
the adversarial graphs can be generated in either supervised or unsupervised
approaches. Based on the Information Bottleneck Principle, we theoretically
prove that our method could obtain a much tighter bound, thus improving the
robustness of graph representation learning. Empirically, we evaluate several
methods on a range of node classification benchmarks and the results
demonstrate GraphACL could achieve comparable accuracy over previous supervised
methods.
