  Deep learning on graphs has attracted significant interest recently. However,
most of the works have focused on (semi-) supervised learning, resulting in
shortcomings including heavy label reliance, poor generalization, and weak
robustness. To address these issues, self-supervised learning (SSL), which
extracts informative knowledge through well-designed pretext tasks without
relying on manual labels, has become a promising and trending learning paradigm
for graph data. Different from other domains like computer vision/natural
language processing, SSL on graphs has an exclusive background, design ideas,
and taxonomies. Under the umbrella of graph self-supervised learning, we
present a timely and comprehensive review of the existing approaches which
employ SSL techniques for graph data. We divide these into four categories
according to the design of their pretext tasks. We further discuss the
remaining challenges and potential future directions in this research field.
