Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction

• URL: http://arxiv.org/abs/2608.29369v1
• Date: Sat, 29 Aug 2026 17:01:44 GMT
• Title: Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction
• Abstract summary: CallosumNet is a graph unlearning framework biologically inspired by corpus structure.<n>It reconstructs subgraphs using biologically-inspired edges; and it restores virtual-temporal subgraphs.<n>It achieves accuracy very close to the gold model.
• Score: 16.96032812958633
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/wenlu-lab/STGraphUnlearning.

Related papers

• Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning [13.722651730453983]
  IsleNet creates balanced, locally coherent subgraphs and reconnects them with lightweight virtual edges.<n>Experiments on real-world benchmarks show that IsleNet attains up to 94% of full-guided accuracy while reducing unlearning time by up to an order of magnitude.
  arXiv  Detail & Related papers  (2026-08-29T16:49:00Z)
• Spatio-Temporal Graph Unlearning [11.249016471516398]
  CallosumNet is a divide-and-Bridging S-temporal graph unlearning framework inspired by the corpus callosum structure.<n>CallosumNet achieves complete unlearning with only 1%-2% relative MAE loss compared to the gold model.
  arXiv  Detail & Related papers  (2025-11-12T15:17:16Z)
• Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies [3.3711670942444014]
  Graph neural networks have emerged as a powerful tool for learning precise interactions.<n>Our method introduces an innovative graph representation where edges are modeled as timestamp nodes.<n>This setup is extended to construct a multi-ship graph that captures spatial interactions while preserving graph sparsity.
  arXiv  Detail & Related papers  (2025-02-20T02:01:40Z)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• Mending of Spatio-Temporal Dependencies in Block Adjacency Matrix [3.529869282529924]
  We propose a novel end-to-end learning architecture designed to mend the temporal dependencies, resulting in a well-connected graph. Our methodology demonstrates superior performance on benchmark datasets, such as SurgVisDom and C2D2.
  arXiv  Detail & Related papers  (2023-10-04T06:42:33Z)
• Recurrent Temporal Revision Graph Networks [17.400798915108957]
  Neighbor aggregation, a critical building block of graph networks, is currently straightforwardly extended from that of static graphs. We propose a novel framework for temporal neighbor aggregation that uses the recurrent neural network with node-wise hidden states. We demonstrate the superior theoretical expressiveness of the proposed framework as well as its state-of-the-art performance in real-world applications.
  arXiv  Detail & Related papers  (2023-09-22T08:09:55Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment [53.72873672076391]
  Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information. We propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information. S2T achieves at most 10.13% performance improvement compared with the state-of-the-art competitors on several datasets.
  arXiv  Detail & Related papers  (2023-02-15T06:36:04Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.