Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning
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Computer Science > Machine Learning
Title:Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning
Abstract: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 this https URL.
| Comments: | Accepted at SIAM International Conference on Data Mining (SDM 2026) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.29360 [cs.LG] |
| (or arXiv:2608.29360v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29360
arXiv-issued DOI via DataCite (pending registration)
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