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GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

arXiv 1803.07294 ↗First posted 20 Mar 2018cs.LG cs.SIcited by 164

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Abstract (arXiv)

We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head's importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three real-world datasets show that our GaAN framework achieves state-of-the-art results on both tasks.

Key facts. Cited by 164 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2018.

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Citing papers per year: 2021: 50; 2022: 46; 2023: 28; 2024: 19; 2025: 11; 2026: 10.

164 papers.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2021How Attentive are Graph Attention Networks?cited by 818
  3. 2025Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Freecited by 266
  4. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  5. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  6. 2021Graph Learning: A Surveycited by 140
  7. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  8. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  9. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  10. 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
  11. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  12. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
38 more
  1. 2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
  2. 2021Graph-MLP: Node Classification without Message Passing in Graphcited by 73
  3. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  4. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  5. 2022Video Transformers: A Surveycited by 63
  6. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  7. 2023GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphscited by 57
  8. 2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
  9. 2022Gradient Gating for Deep Multi-Rate Learning on Graphscited by 49
  10. 2021How Framelets Enhance Graph Neural Networkscited by 46
  11. 20233Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognitioncited by 40
  12. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  13. 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
  14. 2022HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularizationcited by 34
  15. 2022Thermodynamics-informed graph neural networkscited by 31
  16. 2022Graph Lifelong Learning: A Surveycited by 30
  17. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  18. 2021SPAGAN: Shortest Path Graph Attention Networkcited by 28
  19. 2021Learning dynamic and hierarchical traffic spatiotemporal features with Transformercited by 26
  20. 2021Scalable deeper graph neural networks for high-performance materials property predictioncited by 26
  21. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  22. 2021Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full versioncited by 24
  23. 2022Path-Aware Graph Attention for HD Maps in Motion Predictioncited by 23
  24. 2022Graph Neural Networks for Moleculescited by 21
  25. 2021Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 20
  26. 2021FeatureNorm: L2 Feature Normalization for Dynamic Graph Embeddingcited by 19
  27. 2022Improving Graph Neural Networks with Learnable Propagation Operatorscited by 19
  28. 2022Affinity-Aware Graph Networkscited by 17
  29. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  30. 2021TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecastingcited by 16
  31. 2021Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systemscited by 16
  32. 2022pathGCN: Learning General Graph Spatial Operators from Pathscited by 15
  33. 2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsamplingcited by 15
  34. 2022Deep Graph Learning for Anomalous Citation Detectioncited by 14
  35. 2022PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecastingcited by 13
  36. 2022Message Passing Neural Networks for Hypergraphscited by 13
  37. 2021Learning Graph Neural Networks with Positive and Unlabeled Nodescited by 11
  38. 2021GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detectioncited by 11

50 most cited of 164 All 164 (JSON)