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。

走向图对比学习：调查和进展

Towards Graph Contrastive Learning: A Survey and Beyond

Graph-structured data, prevalent in domains ranging from social networks to
biochemical analysis, serve as the foundation for diverse real-world systems.
While graph neural networks demonstrate proficiency in modeling this type of
data, their success is often reliant on significant amounts of labeled data,
posing a challenge in practical scenarios with limited annotation resources. To
tackle this problem, tremendous efforts have been devoted to enhancing graph
machine learning performance under low-resource settings by exploring various
approaches to minimal supervision. In this paper, we introduce a novel concept
of Data-Efficient Graph Learning (DEGL) as a research frontier, and present the
first survey that summarizes the current progress of DEGL. We initiate by
highlighting the challenges inherent in training models with large labeled
data, paving the way for our exploration into DEGL. Next, we systematically
review recent advances on this topic from several key aspects, including
self-supervised graph learning, semi-supervised graph learning, and few-shot
graph learning. Also, we state promising directions for future research,
contributing to the evolution of graph machine learning.

图结构数据是从社交网络到生化分析等领域广泛存在的，它们为不同实际系统提供了基础。而图神经网络在建模这种类型的数据时具有很高的成功率，但通常依赖于大量标记数据，这在实际场景中具有有限的注释资源时带来挑战。为了解决这个问题，我们在低资源环境下增强图机器学习性能进行了巨大努力，探索了各种最小监督方法。本文首次介绍了数据高效图学习 (DEGL) 的新概念作为研究前沿，并总结了当前 DEGL 进展的首份综述。我们首先强调了在使用大量标记数据训练模型时固有的挑战，为我们对 DEGL 的探索铺平道路。接着，我们系统地回顾了最近在这个主题上的一些关键进展，包括自监督图学习、半监督图学习和小样本图学习。我们还提出了未来研究的有望方向，为图机器学习的发展作出了贡献。