Abstract
Research on continual learning (CL) mainly focuses on data represented in the Euclidean space, while research on graph-structured data is scarce. Furthermore, most graph learning models are tailored for static graphs. However, graphs usually evolve continually in the real world. Catastrophic forgetting also emerges in graph learning models when being trained incrementally. This leads to the need to develop robust, effective and efficient continual graph learning approaches. Continual graph learning (CGL) is an emerging area aiming to realize continual learning on graph-structured data. This survey is written to shed light on this emerging area. It introduces the basic concepts of CGL and highlights two unique challenges brought by graphs. Then it reviews and categorizes recent state-of-the-art approaches, analyzing their strategies to tackle the unique challenges in CGL. Besides, it discusses the main concerns in each family of CGL methods, offering potential solutions. Finally, it explores the open issues and potential applications of CGL.
Abstract (translated)
持续图形学习(CGL)是一个新兴的领域,旨在实现基于图形结构数据的持续学习。该研究主要关注在欧氏空间中表示的数据,而对于图形结构数据的研究则相对有限。此外,大多数图形学习模型都是为静态图形设计的。然而,图形通常在现实世界中持续演化。在训练渐进式时,图形学习模型也可能出现灾难性遗忘。这导致了需要开发稳健、有效和高效的持续图形学习方法。持续图形学习(CGL)是一个值得关注的新领域。该研究介绍了CGL的基本概念,并突出了由图形带来的两个独特的挑战。然后,它 reviews and categorizes recent state-of-the-art方法,分析了它们在CGL中应对独特挑战的策略。此外,它还讨论了每个CGL方法家族的主要关注点,并提供潜在解决方案。最后,它探索了CGL的开放问题和潜在应用。
URL
https://arxiv.org/abs/2301.12230
