mlsanityData to 7 Oct 2026

Fast Graph Representation Learning with PyTorch Geometric

arXiv 1903.02428 ↗First posted 6 Mar 2019cs.LG stat.MLcited by 2,414

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Cited by 22 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning and 3D data processing. PyTorch Geometric achieves high data throughput by leveraging sparse GPU acceleration, by providing dedicated CUDA kernels and by introducing efficient mini-batch handling for input examples of different size. In this work, we present the library in detail and perform a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios.

Key facts. Cited by 2,414 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2019.

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Citing papers per year: 2021: 332; 2022: 468; 2023: 468; 2024: 519; 2025: 398; 2026: 229.

2,414 papers.

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  1. 2021How Attentive are Graph Attention Networks?cited by 818
  2. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  3. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  4. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  5. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  6. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  7. 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEscited by 293
  8. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  9. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  10. 2021GRAND: Graph Neural Diffusioncited by 258
  11. 2022Long Range Graph Benchmarkcited by 258
  12. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
38 more
  1. 2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 225
  2. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  3. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  4. 2023SE(3) diffusion model with application to protein backbone generationcited by 214
  5. 2022Multi-Task Learning as a Bargaining Gamecited by 209
  6. 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 199
  7. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  8. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  9. 2022Graph neural networks for materials science and chemistrycited by 181
  10. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  11. 2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 176
  12. 2022Relative representations enable zero-shot latent space communicationcited by 175
  13. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  14. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  15. 2022Revisiting Heterophily For Graph Neural Networkscited by 169
  16. 2021SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequencescited by 159
  17. 2021Equivariant Subgraph Aggregation Networkscited by 154
  18. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  19. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  20. 2021Graph Condensation for Graph Neural Networkscited by 150
  21. 2021GraphiT: Encoding Graph Structure in Transformerscited by 146
  22. 20213D Infomax improves GNNs for Molecular Property Predictioncited by 146
  23. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  24. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 140
  25. 2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 137
  26. 2021Nested Graph Neural Networkscited by 136
  27. 2023Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learningcited by 132
  28. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 127
  29. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 124
  30. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  31. 2022AEGNN: Asynchronous Event-based Graph Neural Networkscited by 118
  32. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  33. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  34. 2022Understanding and Extending Subgraph GNNs by Rethinking Their Symmetriescited by 112
  35. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
  36. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  37. 2022FoSR: First-order spectral rewiring for addressing oversquashing in GNNscited by 104
  38. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102

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