  Deep learning on graphs has recently achieved remarkable success on a variety
of tasks, while such success relies heavily on the massive and carefully
labeled data. However, precise annotations are generally very expensive and
time-consuming. To address this problem, self-supervised learning (SSL) is
emerging as a new paradigm for extracting informative knowledge through
well-designed pretext tasks without relying on manual labels. In this survey,
we extend the concept of SSL, which first emerged in the fields of computer
vision and natural language processing, to present a timely and comprehensive
review of existing SSL techniques for graph data. Specifically, we divide
existing graph SSL methods into three categories: contrastive, generative, and
predictive. More importantly, unlike other surveys that only provide a
high-level description of published research, we present an additional
mathematical summary of existing works in a unified framework. Furthermore, to
facilitate methodological development and empirical comparisons, we also
summarize the commonly used datasets, evaluation metrics, downstream tasks,
open-source implementations, and experimental study of various algorithms.
Finally, we discuss the technical challenges and potential future directions
for improving graph self-supervised learning. Latest advances in graph SSL are
summarized in a GitHub repository
https://github.com/LirongWu/awesome-graph-self-supervised-learning.
