mlsanityData to 14 Oct 2026

Graph Attention Networks

arXiv 1710.10903 ↗First posted 30 Oct 2017stat.ML cs.AI cs.LGcited by 8,923

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Cited by 100 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).

Key facts. Cited by 8,923 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2017.

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Citing papers per year: 2019: 1; 2020: 3; 2021: 1282; 2022: 1472; 2023: 1656; 2024: 1781; 2025: 1567; 2026: 1161.

8,923 papers.

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  1. 2021How Attentive are Graph Attention Networks?cited by 835
  2. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 713
  3. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 478
  4. 2021Rethinking Graph Transformers with Spectral Attentioncited by 473
  5. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  6. 2021Neural Operator: Learning Maps Between Function Spacescited by 414
  7. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  8. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 362
  9. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
  10. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 333
  11. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 329
  12. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
38 more
  1. 2021Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognitioncited by 322
  2. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 320
  3. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 319
  4. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  5. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  6. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  7. 2021Graph Self-Supervised Learning: A Surveycited by 289
  8. 2022Transformer for Partial Differential Equations' Operator Learningcited by 283
  9. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  10. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 279
  11. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 275
  12. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  13. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 270
  14. 2021Combinatorial optimization and reasoning with graph neural networkscited by 268
  15. 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 267
  16. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 264
  17. 2022Long Range Graph Benchmarkcited by 263
  18. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  19. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 259
  20. 2021GRAND: Graph Neural Diffusioncited by 258
  21. 2021A Survey on Deep Semi-supervised Learningcited by 251
  22. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 247
  23. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 243
  24. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  25. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 226
  26. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
  27. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223
  28. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 221
  29. 2023Language is All a Graph Needscited by 220
  30. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  31. 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 214
  32. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 210
  33. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 210
  34. 2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 206
  35. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
  36. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 204
  37. 2023DepGraph: Towards Any Structural Pruningcited by 203
  38. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200

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