  Contrastive learning is an effective unsupervised method in graph
representation learning. Recently, the data augmentation based contrastive
learning method has been extended from images to graphs. However, most prior
works are directly adapted from the models designed for images. 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 are the key
to the performance of contrastive learning models. This leaves much space for
improvement over the existing graph contrastive learning frameworks. In this
work, by introducing an adversarial graph view and an information regularizer,
we propose a simple but effective method, Adversarial Graph Contrastive
Learning (ARIEL), to extract informative contrastive samples within a
reasonable constraint. It consistently outperforms the current graph
contrastive learning methods in the node classification task over various
real-world datasets and further improves the robustness of graph contrastive
learning. The code is at https://github.com/Shengyu-Feng/ARIEL.
