Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction
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Computer Science > Machine Learning
arXiv:2608.29369 (cs)
[Submitted on 29 Aug 2026]
Title:Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction
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Abstract:Spatio-temporal graphs are widely used in modeling complex dynamic processes such as temporal forecasting, molecular dynamics, and healthcare monitoring. Recently, stringent privacy regulations such as GDPR and CCPA have introduced significant new challenges for existing spatio-temporal graph models, requiring complete unlearning of unauthorized data. Since each node in a spatio-temporal graph diffuses information globally across both spatial and temporal dimensions, existing unlearning methods primarily designed for static graphs and localized data removal cannot efficiently erase a single node without incurring costs nearly equivalent to full model retraining. To address this, we propose CallosumNet, a spatio-temporal graph unlearning framework biologically inspired by the corpus callosum structure. CallosumNet makes two key technical contributions: (1) it reconstructs subgraphs using biologically-inspired virtual edges; and (2) it restores interlinked spatio-temporal dependencies among subgraphs via a lightweight meta-graph integration layer. Empirical results on four diverse real-world datasets show that CallosumNet achieves complete unlearning while maintaining accuracy very close to the gold model. The code is publicly available at this https URL.
| Comments: | Accepted as a short paper at ACM SIGSPATIAL 2026. 4 pages |
| Subjects: | Machine Learning (cs.LG); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2608.29369 [cs.LG] |
| (or arXiv:2608.29369v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29369
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction, by Qiming Guo and 4 other authors
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