  Contrastive learning is an effective unsupervised method in graph
representation learning, and the key component of contrastive learning lies in
the construction of positive and negative samples. Previous methods usually
utilize the proximity of nodes in the graph as the principle. Recently, the
data-augmentation-based contrastive learning method has advanced to show great
power in the visual domain, and some works extended this method from images to
graphs. However, unlike the data augmentation on images, the data augmentation
on graphs is far less intuitive and much harder to provide high-quality
contrastive samples, which leaves much space for improvement. In this work, by
introducing an adversarial graph view for data augmentation, we propose a
simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to
extract informative contrastive samples within reasonable constraints. We
develop a new technique called information regularization for stable training
and use subgraph sampling for scalability. We generalize our method from
node-level contrastive learning to the graph level by treating each graph
instance as a super-node. ARIEL consistently outperforms the current graph
contrastive learning methods for both node-level and graph-level classification
tasks on real-world datasets. We further demonstrate that ARIEL is more robust
in the face of adversarial attacks.
