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. Our source code is available at https://github.com/seungyoon-Choi/DSLR_official.

翻译：我们研究了基于回放的图持续学习方法中的回放缓冲区。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，并将其存储在回放缓冲区中，用于后续任务训练。然而，我们发现仅考虑每个回放节点的类别代表性会导致回放节点集中在每个类别的中心区域，从而带来对这些区域节点过拟合的潜在风险，进而加剧灾难性遗忘。此外，由于基于回放的方法严重依赖少量回放节点来保留先前任务的知识，在模型训练中引入具有无关邻居的回放节点可能对模型性能产生显著的负面影响。本文提出了一种名为DSLR的图持续学习模型，具体而言，我们设计了一种覆盖式多样性方法，综合考虑回放节点的类别代表性和类内多样性。同时，我们采用图结构学习来确保回放节点能够连接真正具有信息价值的邻居节点。大量实验结果表明了DSLR的有效性和高效性。我们的源代码已开源至https://github.com/seungyoon-Choi/DSLR_official。