Recently, continual graph learning has been increasingly adopted for diverse graph-structured data processing tasks in non-stationary environments. Despite its promising learning capability, current studies on continual graph learning mainly focus on mitigating the catastrophic forgetting problem while ignoring continuous performance improvement. To bridge this gap, this article aims to provide a comprehensive survey of recent efforts on continual graph learning. Specifically, we introduce a new taxonomy of continual graph learning from the perspective of overcoming catastrophic forgetting. Moreover, we systematically analyze the challenges of applying these continual graph learning methods in improving performance continuously and then discuss the possible solutions. Finally, we present open issues and future directions pertaining to the development of continual graph learning and discuss how they impact continuous performance improvement.

翻译：最近，持续图学习在非平稳环境下多样化的图结构数据处理任务中得到了越来越广泛的应用。尽管其学习能力颇具前景，但当前关于持续图学习的研究主要集中在缓解灾难性遗忘问题，而忽略了持续的性能提升。为弥补这一空白，本文旨在对近期持续图学习的相关工作进行全面综述。具体而言，我们从克服灾难性遗忘的视角引入了一种新的持续图学习分类体系。此外，系统分析了将这些持续图学习方法用于持续提升性能所面临的挑战，并探讨了可能的解决方案。最后，我们提出了与持续图学习发展相关的开放性问题及未来方向，并讨论了它们如何影响持续性能提升。