通过空间熵划分图结构，实现高效精准的时空图数据删除。
Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning
- 基于空间熵构建局部连贯的子图，提升可编辑性。
- 仅重训练受影响子图和轻量虚拟边，保留94%全图精度。
- 适合需快速响应隐私删除请求的交通、医疗等场景。
时空图支撑交通预测、气象预报和医疗监测等应用。隐私法规如GDPR和CCPA要求从训练模型中完全移除未经授权的数据，但在时空图上实现这一目标极具挑战：因空间与时间的消息传递具有全局传播特性，彻底消除某节点影响需代价高昂的全图重训练。时空图去学习需兼顾精确性与效率。本文提出IsleNet，利用空间熵引导的划分策略生成平衡且局部一致的子图，并通过轻量级虚拟边重新连接。当收到去学习请求时，仅需重训练受影响的子图编码器和虚拟边层，确保精确删除且成本极低。在四个真实世界基准上的实验表明，IsleNet在保持高达94%全图准确率的同时，将去学习时间降低了一个数量级。代码已公开于https://github.com/wenlu-lab/STGraphUnlearning。
原文摘要 · Abstract (English)
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.
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