mlsanityData to 7 Oct 2026

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

arXiv 2103.12608 ↗First posted 23 Mar 2021cs.LGcited by 76

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Abstract (arXiv)

Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementing and benchmarking various advanced tasks are still painful and time-consuming with existing libraries. To facilitate graph deep learning research, we introduce DIG: Dive into Graphs, a turnkey library that provides a unified testbed for higher level, research-oriented graph deep learning tasks. Currently, we consider graph generation, self-supervised learning on graphs, explainability of graph neural networks, and deep learning on 3D graphs. For each direction, we provide unified implementations of data interfaces, common algorithms, and evaluation metrics. Altogether, DIG is an extensible, open-source, and turnkey library for researchers to develop new methods and effortlessly compare with common baselines using widely used datasets and evaluation metrics. Source code is available at https://github.com/divelab/DIG.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). Builds on 43 of its 57 references that are in the map. First posted on arXiv 23 Mar 2021.

Builds on

43 of 57 references.

References · 43

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  7. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  8. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
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  11. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  12. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
31 more
  1. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  2. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  3. 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
  4. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  5. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  6. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  7. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  8. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  9. 2020Directional Message Passing for Molecular Graphscited by 556
  10. 2020Deep Graph Contrastive Representation Learningcited by 535
  11. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  12. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  13. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  14. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  15. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  16. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 301
  17. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  18. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 270
  19. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  20. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  21. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  22. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 177
  23. 2020Higher-Order Explanations of Graph Neural Networks via Relevant Walkscited by 138
  24. 2020Self-supervised Learning on Graphs: Deep Insights and New Directioncited by 108
  25. 2020A Systematic Survey on Deep Generative Models for Graph Generationcited by 94
  26. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  27. 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 59
  28. 2020Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discoverycited by 46
  29. 2020Deep Graph Generators: A Surveycited by 36
  30. 2021Efficient Graph Deep Learning in TensorFlow with tf_geometriccited by 17
  31. 2021GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Softwarecited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 24; 2023: 25; 2024: 9; 2025: 7; 2026: 4.

76 papers.

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  1. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
  2. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  3. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 140
  4. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  5. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2022Periodic Graph Transformers for Crystal Material Property Predictioncited by 87
  8. 2022Evaluating Explainability for Graph Neural Networkscited by 85
  9. 2021Robust Counterfactual Explanations on Graph Neural Networkscited by 72
  10. 2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
  11. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  12. 2022Learning Hierarchical Protein Representations via Complete 3D Graph Networkscited by 54
38 more
  1. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
  2. 2023A new perspective on building efficient and expressive 3D equivariant graph neural networkscited by 46
  3. 2022TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoverycited by 45
  4. 2022GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Gamescited by 42
  5. 2023Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasetscited by 37
  6. 2024Complete and Efficient Graph Transformers for Crystal Material Property Predictioncited by 34
  7. 2022Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysiscited by 32
  8. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  9. 2022GraphFM: Improving Large-Scale GNN Training via Feature Momentumcited by 31
  10. 2023Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaborationcited by 30
  11. 2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalizationcited by 29
  12. 2023A Latent Diffusion Model for Protein Structure Generationcited by 28
  13. 2023QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Moleculescited by 26
  14. 2024PPFlow: Target-aware Peptide Design with Torsional Flow Matchingcited by 26
  15. 2023Towards Symmetry-Aware Generation of Periodic Materialscited by 25
  16. 2022Self-Supervised Representation Learning via Latent Graph Predictioncited by 22
  17. 2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
  18. 2022Task-Agnostic Graph Explanationscited by 20
  19. 2022FlowX: Towards Explainable Graph Neural Networks via Message Flowscited by 20
  20. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  21. 2022Automated Data Augmentations for Graph Classificationcited by 19
  22. 2022PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphscited by 18
  23. 2023DEGREE: Decomposition Based Explanation For Graph Neural Networkscited by 18
  24. 2023Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networkscited by 16
  25. 2023LibAUC: A Deep Learning Library for X-Risk Optimizationcited by 15
  26. 2023Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalizationcited by 12
  27. 2022ChemicalX: A Deep Learning Library for Drug Pair Scoringcited by 11
  28. 2023Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networkscited by 11
  29. 2023Graph Structure and Feature Extrapolation for Out-of-Distribution Generalizationcited by 10
  30. 20223D Graph Contrastive Learning for Molecular Property Predictioncited by 9
  31. 2022L2XGNN: Learning to Explain Graph Neural Networkscited by 7
  32. 2022Graph Generative Model for Benchmarking Graph Neural Networkscited by 6
  33. 2021FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networkscited by 5
  34. 2022Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarkscited by 4
  35. 2023Structural Explanations for Graph Neural Networks using HSICcited by 4
  36. 2023PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Networkcited by 4
  37. 2024A Differential Geometric View and Explainability of GNN on Evolving Graphscited by 4
  38. 2021Graph Feature Gating Networkscited by 3

50 most cited of 76 All 76 (JSON)