  Deep models trained in supervised mode have achieved remarkable success on a
variety of tasks. When labeled samples are limited, self-supervised learning
(SSL) is emerging as a new paradigm for making use of large amounts of
unlabeled samples. SSL has achieved promising performance on natural language
and image learning tasks. Recently, there is a trend to extend such success to
graph data using graph neural networks (GNNs). In this survey, we provide a
unified review of different ways of training GNNs using SSL. Specifically, we
categorize SSL methods into contrastive and predictive models. In either
category, we provide a unified framework for methods as well as how these
methods differ in each component under the framework. Our unified treatment of
SSL methods for GNNs sheds light on the similarities and differences of various
methods, setting the stage for developing new methods and algorithms. We also
summarize different SSL settings and the corresponding datasets used in each
setting. To facilitate methodological development and empirical comparison, we
develop a standardized testbed for SSL in GNNs, including implementations of
common baseline methods, datasets, and evaluation metrics.
