  Generalizable, transferrable, and robust representation learning on
graph-structured data remains a challenge for current graph neural networks
(GNNs). Unlike what has been developed for convolutional neural networks (CNNs)
for image data, self-supervised learning and pre-training are less explored for
GNNs. In this paper, we propose a graph contrastive learning (GraphCL)
framework for learning unsupervised representations of graph data. We first
design four types of graph augmentations to incorporate various priors. We then
systematically study the impact of various combinations of graph augmentations
on multiple datasets, in four different settings: semi-supervised,
unsupervised, and transfer learning as well as adversarial attacks. The results
show that, even without tuning augmentation extents nor using sophisticated GNN
architectures, our GraphCL framework can produce graph representations of
similar or better generalizability, transferrability, and robustness compared
to state-of-the-art methods. We also investigate the impact of parameterized
graph augmentation extents and patterns, and observe further performance gains
in preliminary experiments. Our codes are available at
https://github.com/Shen-Lab/GraphCL.
