通过压缩与连接机制，缓解动态图持续学习中的遗忘问题。
CA-DGCL: Dynamic Graph Continual Learning via Condensation and Attachment
- 将历史图快照压缩为紧凑语义表示，保留关键信息。
- 构建跨时间戳节点链并用张量分解稳定节点特征。
- 适合处理动态图中知识留存与新模式学习的场景。
动态图持续学习（DGCL）是应对动态图中灾难性遗忘的有效方法。然而，现有方法对跨图快照的时间信息利用不足。为此，我们提出一种基于压缩与连接的动态图持续学习框架（CA-DGCL）。该方法首先将历史图快照高效压缩为紧凑的语义表示；随后构建跨时间戳的节点链，形成三阶张量，并采用Tucker分解获得稳定的节点特征，以封装历史知识；最后，利用这些特征生成新节点并附加到当前图中，实现过往信息的回放，同时不干扰新模式的学习。此外，引入改进的遗忘度量，更适配动态图设置。大量实验表明，CA-DGCL在抑制遗忘方面优于基线方法，且保持了良好的准确性，验证了其在动态图持续学习中的有效性。
原文摘要 · Abstract (English)
Dynamic graph continual learning (DGCL) is an effective manner for handling catastrophic forgetting in dynamic graphs. However, existing DGCL methods underutilize temporal information across graph snapshots. To address this critical issue, we propose a novel framework for Dynamic Graph Continual Learning via Condensation and Attachment (CA-DGCL). Specifically, CA-DGCL first condenses historical graph snapshots into compact semantic representations efficiently. Further, a cross-timestamp node chains is built to construct a third-order tensor and Tucker decomposition is applied to this tensor for obtaining stable node features, which encapsulate historical knowledge. Finally, these node features are used to generate new nodes and attached to the current graph for replaying of past information without compromising the new patterns. In addtion, a refined forgetting measure is introduced to make it more suitable for dynamic graph settings. Extensive experiments demonstrate that CA-DGCL outperforms baselines in forgetting suppression as well as maintain competitive accuracy, proving its efficacy for dynamic graph continual learning.
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