图上的持续学习：综述
Continual Learning on Graphs: A Survey
AI总结：
本文综述了持续图学习，提出从克服灾难性遗忘角度的新分类法，系统分析性能持续提升的挑战与解决方案，并展望未来方向。
摘要：
近年来，持续图学习在非平稳环境中处理各种图结构数据任务的应用日益广泛。尽管其学习能力令人期待，但当前关于持续图学习的研究主要集中于缓解灾难性遗忘问题，而忽视了持续的性能提升。为弥补这一差距，本文旨在对持续图学习的最新研究进展进行全面综述。具体而言，我们从克服灾难性遗忘的角度引入了一种新的持续图学习分类法。此外，我们系统分析了将这些持续图学习方法应用于持续提升性能所面临的挑战，并讨论了可能的解决方案。最后，我们提出了持续图学习发展中的开放问题与未来方向，并探讨了它们如何影响持续的性能提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
