mlsanityData to 7 Oct 2026

A Comprehensive Survey on Graph Neural Networks

arXiv 1901.00596 ↗First posted 3 Jan 2019cs.LG stat.MLcited by 2,906

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Abstract (arXiv)

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.

Key facts. Cited by 2,906 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jan 2019.

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Citing papers per year: 2021: 559; 2022: 535; 2023: 532; 2024: 562; 2025: 472; 2026: 246.

2,906 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  3. 2021How Attentive are Graph Attention Networks?cited by 818
  4. 2023RWKV: Reinventing RNNs for the Transformer Eracited by 814
  5. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  6. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  7. 2021A Survey of Transformerscited by 399
  8. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 361
  9. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  10. 2021Graph Self-Supervised Learning: A Surveycited by 289
  11. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  12. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
38 more
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  2. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  3. 2023Language is All a Graph Needscited by 219
  4. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  5. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 190
  6. 2022Graph neural networks for materials science and chemistrycited by 181
  7. 2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 176
  8. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  9. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  10. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  11. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  12. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
  13. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  14. 2021End-to-End Constrained Optimization Learning: A Surveycited by 154
  15. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  16. 2021Graph Condensation for Graph Neural Networkscited by 150
  17. 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 145
  18. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  19. 2021Graph Learning: A Surveycited by 140
  20. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  21. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  22. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  23. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  24. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  25. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  26. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  27. 2023Dataset Distillation: A Comprehensive Reviewcited by 126
  28. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  29. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 118
  30. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  31. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 116
  32. 2021Federated Graph Classification over Non-IID Graphscited by 113
  33. 2022Towards Unsupervised Deep Graph Structure Learningcited by 112
  34. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 110
  35. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
  36. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  37. 2022Not too little, not too much: a theoretical analysis of graph (over)smoothingcited by 106
  38. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106

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