mlsanityData to 14 Oct 2026

Curriculum Graph Machine Learning: A Survey

arXiv 2302.02926 ↗First posted 6 Feb 2023cs.LGcited by 15

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Abstract (arXiv)

Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction. Specifically, we first discuss the key challenges of Graph CL and provide its formal problem definition. Then, we categorize and summarize existing methods into three classes based on three kinds of graph machine learning tasks, i.e., node-level, link-level, and graph-level tasks. Finally, we share our thoughts on future research directions. To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 41 of its 76 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

41 of 76 references.

References · 41

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29 more
  1. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  2. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  3. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
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  6. 2019Label Propagation for Deep Semi-supervised Learningcited by 289
  7. 2019On The Power of Curriculum Learning in Training Deep Networkscited by 286
  8. 2021Curriculum Learning: A Surveycited by 259
  9. 2019Competence-based Curriculum Learning for Neural Machine Translationcited by 202
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  11. 2018CurriculumNet: Weakly Supervised Learning from Large-Scale Web Imagescited by 126
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  22. 2022CLNode: Curriculum Learning for Node Classificationcited by 24
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 4; 2026: 3.

15 papers.

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  1. 2024Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matchingcited by 28
  2. 2023Data-centric Graph Learning: A Surveycited by 22
  3. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  4. 2023Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn Firstcited by 10
  5. 2024Mitigating Label Noise on Graph via Topological Sample Selectioncited by 6
  6. 2025Curriculum Guided Personalized Subgraph Federated Learningcited by 4
  7. 2023Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approachcited by 2
  8. 2025Adaptively Point-weighting Curriculum Learningcited by 1
  9. 2024CPT: Competence-progressive Training Strategy for Few-shot Node Classificationcited by 0
  10. 2024Perseus: Leveraging Common Data Patterns with Curriculum Learning for More Robust Graph Neural Networkscited by 0
  11. 2025Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networkscited by 0
  12. 2025Innovative tokenisation of structured data for LLM trainingcited by 0
3 more
  1. 2026Out-of-Distribution Generalization in Graph Foundation Modelscited by 0
  2. 2026Confusion-Aware Transfer Teacher Curriculum Learning Framework: Disentangling Scoring and Pacing Effectscited by 0
  3. 2026CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletioncited by 0