发表机构
Delft University of Technology; Biogen; Texas A&M University - Corpus Christi（代尔夫特理工大学; 渤健公司; 德州农工大学科珀斯克里斯蒂分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对时空图遗忘需兼顾精确性与效率的问题，提出IsleNet，通过空间熵引导分区与轻量虚拟边连接，仅重训练受影响子图，在保持94%全图精度的同时将遗忘时间减至1/10以内。
AI 中文摘要
时空图是交通预测、天气预报、医疗监测等应用的基础。GDPR、CCPA等隐私法规要求从训练好的模型中完全移除未授权数据，但在时空图上实现这一点十分困难：由于信息通过空间和时间消息传递在全局传播，要完全擦除某个节点的影响需要代价高昂的全图重训练。时空图遗忘既需要精确性又需要效率。我们提出IsleNet，该方法使用空间熵引导的分区创建平衡、局部连贯的子图，并通过轻量级虚拟边重新连接这些子图。收到遗忘请求后，仅对受影响的子图编码器和虚拟边层进行重训练，确保以低成本实现精确移除。在四个真实基准上的实验表明，IsleNet的精度达到全图的94%，同时将遗忘时间减少了一个数量级。我们的代码可在此URL公开获取。
英文摘要
Spatiotemporal graphs underpin applications such as traffic forecasting, weather forecasting, and healthcare monitoring. Privacy regulations such as the GDPR and the CCPA require the complete removal of unauthorized data from trained models, but achieving this on a spatiotemporal graph is difficult: because information propagates globally through both spatial and temporal message passing, fully erasing a node's influence forces costly full-graph retraining. ST-graph unlearning requires both exactness and efficiency. We propose IsleNet, which uses spatial-entropy-guided partitioning to create balanced, locally coherent subgraphs and reconnects them with lightweight virtual edges. Upon an unlearning request, only the affected subgraph encoder and virtual-edge layer are retrained, ensuring exact removal with low cost. Experiments on four real-world benchmarks show that IsleNet attains up to 94% of full-graph accuracy while reducing unlearning time by up to an order of magnitude. Our code is publicly available at https://github.com/wenlu-lab/STGraphUnlearning.
CommentsAccepted at SIAM International Conference on Data Mining (SDM 2026)
