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TUDataset: A collection of benchmark datasets for learning with graphs

arXiv 2007.08663 ↗First posted 16 Jul 2020cs.LG cs.NE stat.MLcited by 718

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Abstract (arXiv)

Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDataset for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.

Key facts. Cited by 718 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2020.

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Citing papers per year: 2021: 73; 2022: 125; 2023: 117; 2024: 168; 2025: 126; 2026: 109.

718 papers.

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  1. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  2. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  3. 2024Graph Retrieval-Augmented Generation: A Surveycited by 320
  4. 2021Graph Contrastive Learning Automatedcited by 287
  5. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  6. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  7. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  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. 2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 176
  11. 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
  12. 2021Equivariant Subgraph Aggregation Networkscited by 154
38 more
  1. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  2. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  3. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
  4. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  5. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  6. 2021Federated Graph Classification over Non-IID Graphscited by 113
  7. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  8. 2022Understanding and Extending Subgraph GNNs by Rethinking Their Symmetriescited by 112
  9. 2021Causal Attention for Interpretable and Generalizable Graph Classificationcited by 108
  10. 2022FoSR: First-order spectral rewiring for addressing oversquashing in GNNscited by 104
  11. 2021Breaking the Limits of Message Passing Graph Neural Networkscited by 101
  12. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  13. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 96
  14. 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistancecited by 94
  15. 2024RelBench: A Benchmark for Deep Learning on Relational Databasescited by 90
  16. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  17. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  18. 2021Topological Graph Neural Networkscited by 86
  19. 2024Position: Graph Foundation Models are Already Herecited by 84
  20. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  21. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  22. 2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
  23. 2021Understanding Pooling in Graph Neural Networkscited by 69
  24. 2023Energy Transformercited by 69
  25. 2021Reconstruction for Powerful Graph Representationscited by 65
  26. 2022Structural Entropy Guided Graph Hierarchical Poolingcited by 62
  27. 2022Let Invariant Rationale Discovery Inspire Graph Contrastive Learningcited by 57
  28. 2022GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networkscited by 57
  29. 2022Ordered Subgraph Aggregation Networkscited by 57
  30. 2022GraphWorld: Fake Graphs Bring Real Insights for GNNscited by 56
  31. 2023Interpretable Neural-Symbolic Concept Reasoningcited by 56
  32. 2022Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNscited by 55
  33. 2023Locality-Aware Graph-Rewiring in GNNscited by 55
  34. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  35. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
  36. 2021Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implicationscited by 50
  37. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  38. 2023GSLB: The Graph Structure Learning Benchmarkcited by 49

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