A Survey of Data-Efficient Graph Learning
Wei Ju Siyu Yi Yifan Wang Qingqing Long Junyu Luo Zhiping Xiao Ming Zhang
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.