A timestamp-aware spatio-temporal graph contrastive learning model for network intrusion detection outperforms other self-supervised methods on four datasets while matching supervised GNN performance.
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Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
29 Pith papers cite this work, alongside 379 external citations. Polarity classification is still indexing.
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abstract
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.
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representative citing papers
CausalPOI proposes a spatio-temporal graph causal learning method for cold-start POI check-in forecasting that builds functional interaction graphs and treatment-control pairs to outperform baselines on SafeGraph data.
Bridge augments a graph neural network backbone with time-aware retrieval from a memory of region-time windows to improve cold-start and cross-city urban delivery demand forecasting.
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
AirQualityBench is a realistic global benchmark using hourly data from 3720 stations across 2021-2025 for six pollutants, preserving native missingness masks and evaluating on inverse-transformed physical scales.
SCOT uses Sinkhorn entropic optimal transport to learn explicit soft correspondences between unequal region sets for multi-source cross-city transfer, adding contrastive sharpening and cycle reconstruction for stability and a prototype hub for multi-source alignment.
TRACER presents a training-free closed-loop structured inference framework for recovering physically consistent vehicle motions from sparse accident evidence.
STRP is a granularity-aware model that predicts fine-grained spatio-temporal traffic from coarse inputs via tree convolution and inverse dilated convolution, outperforming baselines on six datasets in window-based and duration-based settings.
STaT is a Symbolic-Temporal-Textual Alignment model that integrates three modalities to reduce shape distortion in non-stationary time series forecasting, reporting up to 8.9% gains in magnitude metrics and 8.5% less distortion on eight benchmarks.
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
A synchronization-safe dynamic microgrid formation method with constraint-aware spatio-temporal graph convolutional networks accelerates distribution system restoration while enforcing safety constraints.
Correlation graphs built from windowed multi-channel sEMG plus a lightweight GNN yield 99% real-time accuracy on five hand gestures from eight subjects, exceeding three prior baselines.
P-K-GCN integrates continuous spline GCN, Koopman linearization, and physics augmentation for spatiotemporal super-resolution on irregular geometries, claiming theoretical error reduction via Rademacher complexity bounds and superior accuracy on cardiac electrodynamics.
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
A plug-and-play learnable Tweedie output head attached to ST-GNN backbones improves RMSE on sparse maritime traffic data by predicting means and learning node-level variance powers via closed-form unit deviance optimization.
PatchSTG partitions sensors into locality-preserving geographic patches and applies dual intra/inter-patch attention to reduce spatiotemporal modeling complexity from quadratic to near-linear while maintaining competitive traffic forecast accuracy.
GC-MoE improves MAE on four traffic forecasting benchmarks by routing nodes to combinations of frozen spatio-temporal GNN experts via a graph-conditioned lightweight router, training only ~17K parameters atop 1.5M frozen weights.
STM3 is a new multiscale Mamba mixture-of-experts model with graph causal networks and contrastive routing that reports state-of-the-art results on 10 long-term spatio-temporal forecasting benchmarks.
Spectrally unstable nodes are identified via graph-spectral distortion analysis as primary drivers of reliability failures; isolating them yields a stable subgraph for learning with propagation-based recovery for the isolated nodes, improving performance across GNNs and spectral clustering under攻击s.
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
A 3D pattern-matching model using Earth Mover's Distance on conflict data outperforms the VIEWS ensemble benchmark in predicting fatalities.
SocialMirror reconstructs 3D meshes of closely interacting humans from monocular videos using semantic guidance from vision-language models and geometric constraints in a diffusion model to handle occlusions and maintain temporal and spatial consistency.
STAGformer forecasts bike-station demand with linear-complexity spatial-temporal agent attention and reports lower RMSE/MAE than listed baselines on NYC and Chicago data.
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SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective
SCOT uses Sinkhorn entropic optimal transport to learn explicit soft correspondences between unequal region sets for multi-source cross-city transfer, adding contrastive sharpening and cycle reconstruction for stability and a prototype hub for multi-source alignment.