mlsanityData to 13 Oct 2026

Automated Machine Learning on Graphs: A Survey

arXiv 2103.00742 ↗First posted 1 Mar 2021cs.LGcited by 39

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Abstract (arXiv)

Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in-depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.

Key facts. Cited by 39 papers in the map (data through 13 Oct 2026). Builds on 70 of its 112 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

70 of 112 references.

References · 70

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  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,911
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38 more
  1. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,100
  2. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
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  4. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,003
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  17. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 6; 2022: 11; 2023: 8; 2024: 8; 2025: 4; 2026: 2.

39 papers.

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  1. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 144
  2. 2023Neural Architecture Search: Insights from 1000 Paperscited by 139
  3. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  4. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  5. 2021Pooling Architecture Search for Graph Classificationcited by 36
  6. 2025Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuningcited by 27
  7. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  8. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  9. 2023Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressionscited by 23
  10. 2024Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuningcited by 21
  11. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  12. 2022Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networkscited by 18
27 more
  1. 2021AutoGL: A Library for Automated Graph Learningcited by 15
  2. 2023Search to Capture Long-range Dependency with Stacking GNNs for Graph Classificationcited by 7
  3. 2023Adversarially Robust Neural Architecture Search for Graph Neural Networkscited by 7
  4. 2022MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learningcited by 6
  5. 2023A Versatile Graph Learning Approach through LLM-based Agentcited by 6
  6. 2023Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networkscited by 5
  7. 2024Unsupervised Graph Neural Architecture Search with Disentangled Self-supervisioncited by 5
  8. 2024Combinatorial Optimization with Automated Graph Neural Networkscited by 5
  9. 2024TinyGraph: Joint Feature and Node Condensation for Graph Neural Networkscited by 5
  10. 2022A Comprehensive Survey on Automated Machine Learning for Recommendationscited by 4
  11. 2024Uncertainty Quantification on Graph Learning: A Surveycited by 4
  12. 2024Automatic Graph Topology-Aware Transformercited by 4
  13. 2025LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Searchcited by 4
  14. 2021Learn Layer-wise Connections in Graph Neural Networkscited by 3
  15. 2021Edge-featured Graph Neural Architecture Searchcited by 2
  16. 2022Search to Pass Messages for Temporal Knowledge Graph Completioncited by 2
  17. 2022Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approachcited by 2
  18. 2024Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsificationcited by 2
  19. 2026Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Searchcited by 2
  20. 2022Graph Neural Networks for Double-Strand DNA Breaks Predictioncited by 1
  21. 2023A hybrid deep-learning-metaheuristic framework for bi-level network design problemscited by 1
  22. 2023Graph Neural Architecture Search with GPT-4cited by 1
  23. 2024Towards Versatile Graph Learning Approach: from the Perspective of Large Language Modelscited by 1
  24. 2025Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendationcited by 1
  25. 2022Efficient Automatic Machine Learning via Design Graphscited by 0
  26. 2025Automated Manifold Learning for Reduced Order Modelingcited by 0
  27. 2026Node-level Graph Neural Architecture Search Frameworkcited by 0