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：我们研究基于回放的图持续学习（GCL）方法中的重放缓冲区。现有基于回放的GCL方法选择每个类别最具代表性的节点，并将其存储在重放缓冲区中，用于后续任务的训练。然而，我们发现仅考虑每个重放节点的类别代表性，会导致重放节点集中在每个类别的中心附近，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高度依赖少量重放节点来保留从先前任务中获得的知识，在模型训练中引入具有无关邻居的重放节点可能对模型性能产生显著的负面影响。在本文中，我们提出一个名为DSLR的GCL模型，具体而言，我们设计了一种覆盖式多样性方法，同时考虑重放节点的类别代表性和每个类别内部的多样性。此外，我们采用图结构学习确保重放节点与真正信息丰富的邻居相连。大量实验结果表明了DSLR的有效性和高效性。我们的源代码可在https://github.com/seungyoon-Choi/DSLR_official获取。