mlsanityData to 7 Oct 2026

AutoGL: A Library for Automated Graph Learning

arXiv 2104.04987 ↗First posted 11 Apr 2021cs.LG cs.AIcited by 14

Not on the map. Why?

Abstract (arXiv)

Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for different graph datasets and tasks is inflexible, labor-intensive, and requires expert knowledge, limiting its adaptivity and applicability. Automated machine learning (AutoML) on graphs, aiming to automatically design the optimal machine learning algorithm for a given graph dataset and task, has received considerable attention. However, none of the existing libraries can fully support AutoML on graphs. To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs. AutoGL is open-source, easy to use, and flexible to be extended. Specifically, we propose a three-layer architecture, consisting of backends to interface with devices, a complete automated graph learning pipeline, and supported graph applications. The automated machine learning pipeline further contains five functional modules: auto feature engineering, neural architecture search, hyper-parameter optimization, model training, and auto ensemble, covering the majority of existing AutoML methods on graphs. For each module, we provide numerous state-of-the-art methods and flexible base classes and APIs, which allow easy usage and customization. We further provide experimental results to showcase the usage of our AutoGL library. We also present AutoGL-light, a lightweight version of AutoGL to facilitate customizing pipelines and enriching applications, as well as benchmarks for graph neural architecture search. The codes of AutoGL are publicly available at https://github.com/THUMNLab/AutoGL.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 45 of its 99 references that are in the map. First posted on arXiv 11 Apr 2021.

Builds on

45 of 99 references.

References · 45

  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. 2018How Powerful are Graph Neural Networks?cited by 4,355
  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. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  9. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  10. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  11. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  12. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
33 more
  1. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  2. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  3. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  4. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  5. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  6. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  7. 2019Heterogeneous Graph Attention Networkcited by 732
  8. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  9. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 612
  10. 2020Heterogeneous Graph Transformercited by 581
  11. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  12. 2019Graph U-Netscited by 489
  13. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  14. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 411
  15. 2018Deep Learning on Graphs: A Surveycited by 393
  16. 2021Graph Self-Supervised Learning: A Surveycited by 289
  17. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  18. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 253
  19. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  20. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  21. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  22. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 169
  23. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 166
  24. 2019AliGraph: A Comprehensive Graph Neural Network Platformcited by 152
  25. 2020A Survey on Evolutionary Neural Architecture Searchcited by 142
  26. 2019PyTorch-BigGraph: A Large-scale Graph Embedding Systemcited by 118
  27. 2019Learning Disentangled Representations for Recommendationcited by 110
  28. 2019Auto-GNN: Neural Architecture Search of Graph Neural Networkscited by 73
  29. 2018Automated Machine Learning: From Principles to Practicescited by 62
  30. 2018NetLSD: Hearing the Shape of a Graphcited by 51
  31. 2021Automated Machine Learning on Graphs: A Surveycited by 38
  32. 2019Power up! Robust Graph Convolutional Network via Graph Poweringcited by 15
  33. 2020Eigen-GNN: A Graph Structure Preserving Plug-in for GNNscited by 14

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 4; 2024: 6; 2025: 1.

14 papers.

Sort:

By area:

  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
  3. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  4. 2024Spectral Invariant Learning for Dynamic Graphs under Distribution Shiftscited by 18
  5. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  6. 2023Knowledge Graph Completion with Counterfactual Augmentationcited by 13
  7. 2024GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaborationcited by 12
  8. 2024Path-based Explanation for Knowledge Graph Completioncited by 11
  9. 2021Adversarial Attack Framework on Graph Embedding Models with Limited Knowledgecited by 10
  10. 2022Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classificationcited by 8
  11. 2023Adversarially Robust Neural Architecture Search for Graph Neural Networkscited by 7
  12. 2023Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Predictioncited by 5
2 more
  1. 2024Unsupervised Graph Neural Architecture Search with Disentangled Self-supervisioncited by 5
  2. 2024Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsificationcited by 2