DynaSTar is proposed, a Dynamic Spatio-Temporal Graph Invariant Learning model designed for reliable out-of-time (OOT) traffic prediction under evolving topologies, which employs a dynamic probabilistic graph structure, which is continuously refined through momentum-based updates and differentiable sparse sampling to model evolving inter-node dependencies.
This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.
Zhengxu Luan, Huan Wang, Miaobowen Wang et al.· Computers and artificial int...· 0 citations
A spatiotemporal graph Transformer framework that jointly models spatial interactions and temporal dependencies for traffic forecasting in edge computing and leverages Transformer-based self-attention to learn long-range temporal patterns from historical traffic observations is proposed.
The Structure-Guided Spatiotemporal Attention Graph Neural Network is proposed, offering a mechanistic account of the model's decision-making process while ensuring robust forecasting by aligning attention-based reasoning with identified macroscopic dependencies and preventing over-reliance on ephemeral local noise.
A deeply fused GraphSAGE-GRU cell that embeds independent inductive GraphSAGE(SAmple and aggreGatE) encoders directly into each GRU gate, enabling simultaneous spatio-temporal feature extraction at every time step while remaining topology-agnostic.
Xuran Chen· Poster Volume 0008 The 2026...· 0 citations
Accurately capturing time-varying spatial dependencies and inherent spatio-temporal heterogeneity remains a significant challenge in traffic flow predic-tion. Traditional methods often rely on predefined static graphs, which fail to adapt to the dynamic and periodic nature of urban traffic. To address these limitations, we propose a novel framework named PDMetaNet (Periodic Dy-namic graph with Meta-graph Memory Network). Specifically, a Periodic Dynamic Graph Generation Module (PDGM) is developed to construct bidi-rectional dynamic adjacency matrices by synergistically fusing real-time traf-fic signals with intra-day and intra-week periodicities. Building upon this, the Periodic Adaptive Graph Convolutional Recurrent Unit (PAGCRU) integrates dynamic graph convolutions with gated recurrent mechanisms to facilitate joint spatiotemporal feature modeling. Furthermore, a Spatio-Temporal Meta-Graph Learner (STMG) is incorporated to maintain a persistent bank of meta-nodes representing prototypical traffic patterns across different periods. This mechanism enables the model to effectively reuse historical knowledge and optimize the dynamic graph topology through an attention-based querying process. To enhance the robustness and generalization of the memory pa-rameters, contrastive and consistency losses are introduced as structural con-straints. Extensive experiments on the PeMS04 and PeMS08 datasets demon-strate that PDMetaNet significantly outperforms ten state-of-the-art baselines, achieving a substantial reduction in prediction error and exhibiting superior capability in capturing complex spatiotemporal dynamics.
Jianxuan Wei· Poster Volume 0008 The 2026...· 0 citations
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.
Qi-Ming Guo, Wenbo Sun, Chen Pan et al.· 0 citations
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