Continual learning (CL) is the research field that aims to build machine learning models that can accumulate knowledge continuously over different tasks without retraining from scratch. Previous studies have shown that pre-training graph neural networks (GNN) may lead to negative transfer (Hu et al., 2020) after fine-tuning, a setting which is closely related to CL. Thus, we focus on studying GNN in the continual graph learning (CGL) setting. We propose the first continual graph learning benchmark for spatio-temporal graphs and use it to benchmark well-known CGL methods in this novel setting. The benchmark is based on the N-UCLA and NTU-RGB+D datasets for skeleton-based action recognition. Beyond benchmarking for standard performance metrics, we study the class and task-order sensitivity of CGL methods, i.e., the impact of learning order on each class/task's performance, and the architectural sensitivity of CGL methods with backbone GNN at various widths and depths. We reveal that task-order robust methods can still be class-order sensitive and observe results that contradict previous empirical observations on architectural sensitivity in CL.

翻译：持续学习（CL）是旨在构建能够在不同任务间持续积累知识而无需从头重新训练的机器学习模型的研究领域。先前研究表明，预训练图神经网络（GNN）在微调后可能导致负迁移（Hu 等，2020），这一设置与持续学习密切相关。因此，我们专注于在持续图学习（CGL）设置下研究GNN。我们提出了首个针对时空图的持续图学习基准，并用于在此新设置下评估知名的CGL方法。该基准基于N-UCLA和NTU-RGB+D数据集进行基于骨架的动作识别。除了标准性能指标的基准测试外，我们研究了CGL方法对类别和任务顺序的敏感性，即学习顺序对每个类别/任务性能的影响，以及CGL方法在不同宽度和深度的骨干GNN上的架构敏感性。我们发现，对任务顺序鲁棒的方法仍可能对类别顺序敏感，并观察到与先前关于CL中架构敏感性的实证观察相矛盾的结果。