mlsanityData to 7 Oct 2026

Open Graph Benchmark: Datasets for Machine Learning on Graphs

arXiv 2005.00687 ↗First posted 2 May 2020cs.LG cs.SI stat.MLcited by 2,148

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Abstract (arXiv)

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https://ogb.stanford.edu .

Key facts. Cited by 2,148 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2020.

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Citing papers per year: 2021: 240; 2022: 361; 2023: 443; 2024: 492; 2025: 332; 2026: 280.

2,148 papers.

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  1. 2021How Attentive are Graph Attention Networks?cited by 818
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  3. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  4. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  5. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  6. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  7. 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentationcited by 339
  8. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  9. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  10. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  11. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  12. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
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  1. 2022A Survey of Machine Unlearningcited by 298
  2. 2021Graph Self-Supervised Learning: A Surveycited by 289
  3. 2021Graph Contrastive Learning Automatedcited by 287
  4. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  5. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  6. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  7. 2021GRAND: Graph Neural Diffusioncited by 258
  8. 2022Long Range Graph Benchmarkcited by 258
  9. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  10. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 253
  11. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  12. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223
  13. 2023Language is All a Graph Needscited by 219
  14. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  15. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 209
  16. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  17. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  18. 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 199
  19. 2021Identity-aware Graph Neural Networkscited by 197
  20. 2022Representing Long-Range Context for Graph Neural Networks with Global Attentioncited by 193
  21. 2022Structure-Aware Transformer for Graph Representation Learningcited by 191
  22. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 190
  23. 2022Discovering Invariant Rationales for Graph Neural Networkscited by 186
  24. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  25. 2024LLaGA: Large Language and Graph Assistantcited by 177
  26. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  27. 2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 176
  28. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  29. 2022Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Modelscited by 172
  30. 2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
  31. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  32. 2023Exphormer: Sparse Transformers for Graphscited by 166
  33. 2022Handling Distribution Shifts on Graphs: An Invariance Perspectivecited by 163
  34. 2024The Road Less Scheduledcited by 158
  35. 2021Equivariant Subgraph Aggregation Networkscited by 154
  36. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  37. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  38. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 153

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