解决动态图学习中节点遗忘问题，通过压缩和拼接历史信息提升模型稳定性。
Condensation-Concatenation Framework for Dynamic Graph Continual Learning
- 将历史图快照压缩为紧凑语义表示，保留标签分布与拓扑特性。
- 在新图上选择性拼接历史嵌入，缓解结构变化带来的遗忘。
- 设计更适配动态图的遗忘度量，精准捕捉节点性能退化。
动态图在现实场景中普遍存在，持续的结构变化会导致图神经网络出现灾难性遗忘。尽管持续学习已被拓展至动态图，但现有方法忽视了拓扑变化对已有节点的影响。为此，我们提出一种新的动态图持续学习框架——基于压缩-拼接的持续学习（CCC）。具体而言，CCC首先将历史图快照压缩为紧凑的语义表示，同时力求保持原始标签分布和拓扑特性；随后，选择性地将这些历史嵌入与当前图表示拼接。此外，我们改进了遗忘度量（FM），通过量化结构更新导致的已有节点预测性能下降，使其更适应动态图场景。在四个真实数据集上的大量实验表明，CCC在性能上优于现有最先进基线方法。
原文摘要 · Abstract (English)
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.
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