Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to repeated full-snapshot retraining and are not well-suited for collaborative settings with partitioned data. In realistic graph systems, cross-partition edges are unavoidable, but direct sharing of graph structure between clients may violate privacy constraints. We propose DG-CoLearn, a client-oblivious collaborative dynamic graph learning framework built on incremental graph snapshot processing, which focuses computation on graph regions affected by temporal updates while preserving historical information through temporal modelling. This incremental design is consistently applied across the entire graph processing pipeline, including a server-mediated embedding exchange mechanism to enable accurate multi-hop message passing without exposing raw cross-client structural information. Extensive experiments demonstrate that DG-CoLearn achieves up to 33.8$\times$ speedup in training time and 27.4$\times$ reduction in communication overhead, while consistently improving predictive performance on both node classification (up to 13.36% F1 improvement) and link prediction (up to 8.27% MAP improvement) tasks. These results highlight the effectiveness of DG-CoLearn in bridging efficiency, scalability, and client-to-client structural privacy in collaborative dynamic graph learning.

翻译：动态图学习（DGL）对于建模演化中的图数据至关重要，但现有方法因需反复进行全快照重训练而存在显著计算开销，且难以适配数据分区的协作环境。在真实图系统中，跨分区边难以避免，但客户端间直接共享图结构可能违反隐私约束。我们提出DG-CoLearn，一种基于增量图快照处理的客户端无关协作动态图学习框架，将计算聚焦于受时间更新影响的图区域，同时通过时间建模保留历史信息。该增量设计贯穿整个图处理流程，包括一种服务器中介的嵌入交换机制，可在不暴露原始跨客户端结构信息的前提下实现精准的多跳消息传递。大量实验表明，DG-CoLearn在训练时间上最多提速33.8倍，通信开销降低27.4倍，同时在节点分类（F1值最高提升13.36%）和链路预测（MAP最高提升8.27%）任务上持续提升预测性能。这些结果突显了DG-CoLearn在协作动态图学习中兼顾效率、可扩展性与客户端间结构隐私的有效性。