We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods. Existing rehearsal-based GCL methods select the most representative nodes for each class and store them in a replay buffer for later use in training subsequent tasks. However, we discovered that considering only the class representativeness of each replayed node makes the replayed nodes to be concentrated around the center of each class, incurring a potential risk of overfitting to nodes residing in those regions, which aggravates catastrophic forgetting. Moreover, as the rehearsal-based approach heavily relies on a few replayed nodes to retain knowledge obtained from previous tasks, involving the replayed nodes that have irrelevant neighbors in the model training may have a significant detrimental impact on model performance. In this paper, we propose a GCL model named DSLR, specifically, we devise a coverage-based diversity (CD) approach to consider both the class representativeness and the diversity within each class of the replayed nodes. Moreover, we adopt graph structure learning (GSL) to ensure that the replayed nodes are connected to truly informative neighbors. Extensive experimental results demonstrate the effectiveness and efficiency of DSLR.

翻译：我们针对图持续学习方法中的回放缓冲区展开研究。现有基于回放的图持续学习方法为每个类别选择最具代表性的节点，并将其存储于回放缓冲区中，供后续任务训练使用。然而，我们发现仅考虑回放节点的类别代表性，会导致回放节点聚集于各类别中心区域，易引发对区域内部节点的过拟合风险，进而加剧灾难性遗忘。此外，由于回放方法严重依赖少量回放节点保留先前任务的知识，在模型训练中引入带有无关邻居的回放节点可能对模型性能造成显著负面影响。本文提出名为DSLR的图持续学习模型：具体而言，我们设计了覆盖式多样性方法，在回放节点选择中兼顾类别代表性与类内多样性；同时采用图结构学习确保回放节点与真正信息性邻居建立连接。大量实验结果表明了DSLR的有效性与高效性。