mlsanityData to 7 Oct 2026

Meta-Learning with Graph Neural Networks: Methods and Applications

arXiv 2103.00137 ↗First posted 27 Feb 2021cs.LG cs.AIcited by 7

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Abstract (arXiv)

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different meta-learning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 49 of its 78 references that are in the map. First posted on arXiv 27 Feb 2021.

Builds on

49 of 78 references.

References · 49

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37 more
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Built on by

Cited by year

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

7 papers.

  1. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  2. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  3. 2022Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regressioncited by 2
  4. 2023A hybrid deep-learning-metaheuristic framework for bi-level network design problemscited by 1
  5. 2024Graph Size-imbalanced Learning with Energy-guided Structural Smoothingcited by 1
  6. 2022Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stationscited by 0
  7. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0