图上的持续学习：挑战、解决方案与机遇
Continual Learning on Graphs: Challenges, Solutions, and Opportunities
AI总结：
本文系统综述了图上持续学习（CGL）的任务设置、算法分类、基准及与传统持续学习的对比，并探讨了未来研究方向。
摘要：
图数据上的持续学习近期引起了极大的关注，其目标是在适应顺序更新的模型以应对新出现的图任务的同时，解决现有任务上的灾难性遗忘问题。尽管已有努力总结在欧几里得数据（例如图像和文本）上的持续学习研究进展，但对图上的持续学习（又称持续图学习，CGL，或终身图学习）进展的系统性回顾仍然十分迫切。图数据在数据结构和应用场景方面要复杂得多，使得CGL的任务设置、模型设计和应用极具挑战性。为了弥合这一差距，我们通过阐明不同的任务设置并根据特征对现有方法进行分类，对现有的持续图学习（CGL）算法进行了全面回顾。我们将CGL方法与传统的持续学习技术进行了比较，并分析了传统持续学习技术在CGL任务中的适用性。此外，我们回顾了对CGL研究至关重要的基准工作。最后，我们讨论了剩余的挑战并提出了几个未来的方向。我们将维护一个最新的GitHub仓库，其中包含CGL算法的全面列表，可通过https://github.com/UConn-DSIS/Survey-of-Continual-Learning-on-Graphs访问。
英文摘要：
Continual learning on graph data has recently attracted paramount attention for its aim to resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially updated model to newly emerged graph tasks. While there have been efforts to summarize progress on continual learning research over Euclidean data, e.g., images and texts, a systematic review of progress in continual learning on graphs, a.k.a, continual graph learning (CGL) or lifelong graph learning, is still demanding. Graph data are far more complex in terms of data structures and application scenarios, making CGL task settings, model designs, and applications extremely challenging. To bridge the gap, we provide a comprehensive review of existing continual graph learning (CGL) algorithms by elucidating the different task settings and categorizing the existing methods based on their characteristics. We compare the CGL methods with traditional continual learning techniques and analyze the applicability of the traditional continual learning techniques to CGL tasks. Additionally, we review the benchmark works that are crucial to CGL research. Finally, we discuss the remaining challenges and propose several future directions. We will maintain an up-to-date GitHub repository featuring a comprehensive list of CGL algorithms, accessible at https://github.com/UConn-DSIS/Survey-of-Continual-Learning-on-Graphs.
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。
