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STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.trc.2014.02.006openalex-snapshot

Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

inferredcites →https://doi.org/10.1049/iet-its.2016.0208openalex-snapshot

LSTM network: a deep learning approach for short‐term traffic forecast

inferredcites →https://doi.org/10.1061/(asce)0733-947x(2003)129:6(664)openalex-snapshot

Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

inferredcites →https://doi.org/10.1098/rsta.2020.0209openalex-snapshot

Time-series forecasting with deep learning: a survey

inferredcites →https://doi.org/10.1109/cvpr.2019.01096openalex-snapshot

Attention Branch Network: Learning of Attention Mechanism for Visual Explanation

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00563openalex-snapshot

PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling

inferredcites →https://doi.org/10.1109/iccv.2019.00936openalex-snapshot

DeepGCNs: Can GCNs Go As Deep As CNNs?

inferredcites →https://doi.org/10.1109/icis46139.2019.8940265openalex-snapshot

Time Series Prediction Based on Temporal Convolutional Network

inferredcites →https://doi.org/10.1109/tkde.2021.3056502openalex-snapshot

Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting

inferredcites →https://doi.org/10.1109/tpami.2021.3120012openalex-snapshot

Adaptive Graph Guided Disambiguation for Partial Label Learning

inferredcites →https://doi.org/10.1145/3292500.3330961openalex-snapshot

Heterogeneous Graph Neural Network

inferredcites →https://doi.org/10.1145/3394486.3403076openalex-snapshot

Towards Deeper Graph Neural Networks

inferredcites →https://doi.org/10.1145/3394486.3403118openalex-snapshot

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

inferredcites →https://doi.org/10.1145/3394486.3403177openalex-snapshot

AM-GCN: Adaptive Multi-channel Graph Convolutional Networks

inferredcites →https://doi.org/10.1609/aaai.v32i1.12328openalex-snapshot

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

inferredcites →https://doi.org/10.1609/aaai.v33i01.3301922openalex-snapshot

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

inferredcites →https://doi.org/10.1609/aaai.v34i01.5438openalex-snapshot

Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

inferredcites →https://doi.org/10.1609/aaai.v34i01.5477openalex-snapshot

GMAN: A Graph Multi-Attention Network for Traffic Prediction

inferredcites →https://doi.org/10.1609/aaai.v35i5.16542openalex-snapshot

Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

inferredcites →https://doi.org/10.17863/cam.48429openalex-snapshot

Graph Attention Networks

inferredcites →https://doi.org/10.24963/ijcai.2018/505openalex-snapshot

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

inferredcites →https://doi.org/10.24963/ijcai.2019/264openalex-snapshot

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

inferredcites →https://doi.org/10.24963/ijcai.2020/326openalex-snapshot

LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks

inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot

Semi-Supervised Classification with Graph Convolutional Networks

inferredcites →https://doi.org/10.48550/arxiv.1707.01926openalex-snapshot

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

inferredcites →https://doi.org/10.48550/arxiv.2006.13009openalex-snapshot

Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

inferredcites →https://doi.org/10.48550/arxiv.2007.02842openalex-snapshot

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed

inferred← citeshttps://doi.org/10.1002/widm.70024openalex-snapshot

Exploring Causal Learning Through Graph Neural Networks: An In‐Depth Review

inferred← citeshttps://doi.org/10.1007/978-981-95-3324-4_35openalex-snapshot

Crowd Gathering Hotspot Prediction Across Multiple Cameras via Integrated Anomalous Aggregation Modeling

inferred← citeshttps://doi.org/10.1007/978-981-95-4384-7_17openalex-snapshot

Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks

inferred← citeshttps://doi.org/10.1007/s10044-026-01626-xopenalex-snapshot

Dynamic fusion graph convolutional traffic flow forecasting model with external factors and multi-period features enhanced

inferred← citeshttps://doi.org/10.1016/j.eswa.2026.134153openalex-snapshot

An integrated multi-source information fusion framework for accurate and explainable short-term traffic flow forecasting: Joint spatiotemporal decomposition with hierarchical graph memory networks

inferred← citeshttps://doi.org/10.1016/j.ipm.2024.103797openalex-snapshot

Text-enhanced knowledge graph representation learning with local structure

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.129025openalex-snapshot

Multi-View topology assisted dynamic graph learning for fMRI-based Alzheimer’s disease identification

inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108269openalex-snapshot

A systematic literature review of spatio-temporal graph neural network models for time series forecasting and classification

inferred← citeshttps://doi.org/10.1016/j.physa.2024.129636openalex-snapshot

Multistep traffic speed prediction: A sequence-to-sequence spatio-temporal attention model

inferred← citeshttps://doi.org/10.1049/itr2.70243openalex-snapshot

Multi‐Step Ahead Traffic State Prediction Using a Stochastic Residual Long Short‐Term Memory and Temporal Dilated Attention Mechanism

inferred← citeshttps://doi.org/10.1109/access.2023.3300232openalex-snapshot

Spatio-Temporal Residual Graph Convolutional Network for Short-Term Traffic Flow Prediction

inferred← citeshttps://doi.org/10.1111/jfr3.70022openalex-snapshot

Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed

inferred← citeshttps://doi.org/10.1142/s0218126625500458openalex-snapshot

Enhancing Road Traffic Prediction Using Data Preprocessing Optimization

inferred← citeshttps://doi.org/10.1145/3748636.3762721openalex-snapshot

CausalGRIT: Causal Graph Reasoning for Traffic Congestion Prediction – From Statistical Association to Casual Intervention

inferred← citeshttps://doi.org/10.3390/a17090398openalex-snapshot

In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review

inferred← citeshttps://doi.org/10.3390/en18040993openalex-snapshot

Transient Stability Analysis and Emergency Generator Tripping Control Based on Spatio-Temporal Graph Deep Learning

inferred← citeshttps://doi.org/10.3390/ijgi12060241openalex-snapshot

PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting

inferred← citeshttps://doi.org/10.3390/math11061540openalex-snapshot

Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments

inferred← citeshttps://doi.org/10.3390/math13213541openalex-snapshot

Efficient Image Segmentation of Coal Blocks Using an Improved DIRU-Net Model

inferred← citeshttps://doi.org/10.3390/su15043520openalex-snapshot

Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations

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