The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor performance of machine learning models. To overcome this challenge, class-imbalanced learning on graphs (CILG) has emerged as a promising solution that combines the strengths of graph representation learning and class-imbalanced learning. In recent years, significant progress has been made in CILG. Anticipating that such a trend will continue, this survey aims to offer a comprehensive understanding of the current state-of-the-art in CILG and provide insights for future research directions. Concerning the former, we introduce the first taxonomy of existing work and its connection to existing imbalanced learning literature. Concerning the latter, we critically analyze recent work in CILG and discuss urgent lines of inquiry within the topic. Moreover, we provide a continuously maintained reading list of papers and code at https://github.com/yihongma/CILG-Papers.

翻译：数据驱动研究的快速发展增加了对有效图数据分析的需求。然而，现实世界的数据往往存在类别不平衡问题，导致机器学习模型性能不佳。为应对这一挑战，图上的类别不平衡学习（CILG）作为一种结合图表示学习和类别不平衡学习优势的解决方案应运而生。近年来，CILG领域取得了显著进展。鉴于这一趋势将持续，本综述旨在全面理解CILG的当前最新进展，并为未来研究方向提供洞见。就前者而言，我们首次对现有工作进行分类，并阐述其与现有不平衡学习文献的联系。就后者而言，我们批判性地分析了CILG领域的近期工作，并讨论了该主题中亟需探究的方向。此外，我们提供了一个持续更新的论文与代码阅读清单，地址为https://github.com/yihongma/CILG-Papers。