Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning

• URL: http://arxiv.org/abs/2608.29360v1
• Date: Sat, 29 Aug 2026 16:49:00 GMT
• Title: Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning
• Abstract summary: 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.
• Score: 13.722651730453983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/wenlu-lab/STGraphUnlearning.

Related papers

• Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction [16.96032812958633]
  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.
  arXiv  Detail & Related papers  (2026-08-29T17:01:44Z)
• 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)
• ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data [59.78770412981611]
  In real-world applications, most nodes may not possess any available temporal data during training.<n>We propose a principled framework named ST-FiT to handle this problem.
  arXiv  Detail & Related papers  (2024-12-14T17:51:29Z)
• Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks [5.514795777097036]
  We introduce the concept of the Graph Winning Ticket (GWT), derived from the Lottery Ticket Hypothesis (LTH) By adopting a pre-determined star topology as a GWT prior to training, we balance edge reduction with efficient information propagation. Our approach enables training ASTGNNs on the largest scale spatial-temporal dataset using a single A6000 equipped with 48 GB of memory.
  arXiv  Detail & Related papers  (2024-06-12T14:53:23Z)
• 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)
• Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning [30.23894624193583]
  Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs. We develop a unified data-model dynamic sparsity framework named Graph Decantation (GraphDec) to address challenges brought by training upon a massive class-imbalanced graph data.
  arXiv  Detail & Related papers  (2022-10-01T01:47:00Z)
• Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting [4.1700160312787125]
  We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
  arXiv  Detail & Related papers  (2022-06-07T09:08:35Z)
• 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)
• Distributed Training of Graph Convolutional Networks using Subgraph Approximation [72.89940126490715]
  We propose a training strategy that mitigates the lost information across multiple partitions of a graph through a subgraph approximation scheme. The subgraph approximation approach helps the distributed training system converge at single-machine accuracy.
  arXiv  Detail & Related papers  (2020-12-09T09:23:49Z)
• Spatio-Temporal Graph Scattering Transform [54.52797775999124]
  Graph neural networks may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. We put forth a novel mathematically designed framework to analyze-temporal data.
  arXiv  Detail & Related papers  (2020-12-06T19:49:55Z)
• Unrolling of Deep Graph Total Variation for Image Denoising [106.93258903150702]
  In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design. We employ interpretable analytical low-pass graph filters and employ 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN.
  arXiv  Detail & Related papers  (2020-10-21T20:04:22Z)

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.