Dynamic graphs are prevalent in real-world scenarios, where continuous structural changes induce catastrophic forgetting in graph neural networks (GNNs). While continual learning has been extended to dynamic graphs, existing methods overlook the effects of topological changes on existing nodes. To address it, we propose a novel framework for continual learning on dynamic graphs, named Condensation-Concatenation-based Continual Learning (CCC). Specifically, CCC first condenses historical graph snapshots into compact semantic representations while aiming to preserve the original label distribution and topological properties. Then it concatenates these historical embeddings with current graph representations selectively. Moreover, we refine the forgetting measure (FM) to better adapt to dynamic graph scenarios by quantifying the predictive performance degradation of existing nodes caused by structural updates. CCC demonstrates superior performance over state-of-the-art baselines across four real-world datasets in extensive experiments.

翻译：动态图在现实场景中普遍存在，其持续的结构变化会导致图神经网络（GNNs）出现灾难性遗忘。尽管持续学习已被扩展至动态图领域，现有方法往往忽视了拓扑变化对已有节点的影响。为解决这一问题，我们提出了一种新颖的动态图持续学习框架，命名为基于凝练-拼接的持续学习（CCC）。具体而言，CCC首先将历史图快照凝练为紧凑的语义表示，同时力求保留原始的标签分布与拓扑特性；随后，它将这些历史嵌入与当前图表示进行选择性拼接。此外，我们改进了遗忘度量（FM），通过量化结构更新对已有节点预测性能的退化影响，使其更好地适应动态图场景。在四个真实世界数据集上的大量实验表明，CCC在性能上显著优于当前最先进的基线方法。