In recent years, deep learning on graphs has achieved remarkable success in
various domains. However, the reliance on annotated graph data remains a
significant bottleneck due to its prohibitive cost and time-intensive nature.
To address this challenge, self-supervised learning (SSL) on graphs has gained
increasing attention and has made significant progress. SSL enables machine
learning models to produce informative representations from unlabeled graph
data, reducing the reliance on expensive labeled data. While SSL on graphs has
witnessed widespread adoption, one critical component, Graph Contrastive
Learning (GCL), has not been thoroughly investigated in the existing
literature. Thus, this survey aims to fill this gap by offering a dedicated
survey on GCL. We provide a comprehensive overview of the fundamental
principles of GCL, including data augmentation strategies, contrastive modes,
and contrastive optimization objectives. Furthermore, we explore the extensions
of GCL to other aspects of data-efficient graph learning, such as weakly
supervised learning, transfer learning, and related scenarios. We also discuss
practical applications spanning domains such as drug discovery, genomics
analysis, recommender systems, and finally outline the challenges and potential
future directions in this field.

在图数据上的深度学习、自监督学习、图对比学习和以数据为基础的图学习的实际应用是这篇综述的主要内容。