mlsanityData to 13 Oct 2026

Graph Neural Networks: A Review of Methods and Applications

arXiv 1812.08434 ↗First posted 20 Dec 2018cs.LG cs.AI stat.MLcited by 1,574

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Cited by 23 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.

Key facts. Cited by 1,574 papers in the map (data through 13 Oct 2026). First posted on arXiv 20 Dec 2018.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 279; 2022: 295; 2023: 325; 2024: 341; 2025: 235; 2026: 99.

1,574 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,112
  2. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 879
  3. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 469
  4. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 331
  5. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  6. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  7. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  8. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 247
  9. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  10. 2023Language is All a Graph Needscited by 219
  11. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  12. 2023Data-centric Artificial Intelligence: A Surveycited by 202
38 more
  1. 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 201
  2. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  3. 2022Graph neural networks for materials science and chemistrycited by 181
  4. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 170
  5. 2021End-to-End Constrained Optimization Learning: A Surveycited by 155
  6. 2021Graph Condensation for Graph Neural Networkscited by 150
  7. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  8. 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 146
  9. 2021GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graphcited by 143
  10. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 142
  11. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  12. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  13. 2022Graph-Coupled Oscillator Networkscited by 124
  14. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
  15. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  16. 2021A Unified Lottery Ticket Hypothesis for Graph Neural Networkscited by 118
  17. 2022AEGNN: Asynchronous Event-based Graph Neural Networkscited by 118
  18. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  19. 2021Geometric Deep Learning on Molecular Representationscited by 113
  20. 2022Disentangled Representation Learningcited by 112
  21. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 111
  22. 2021MagNet: A Neural Network for Directed Graphscited by 106
  23. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  24. 2022FoSR: First-order spectral rewiring for addressing oversquashing in GNNscited by 105
  25. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  26. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  27. 2023Specformer: Spectral Graph Neural Networks Meet Transformerscited by 102
  28. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
  29. 2024Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agentscited by 96
  30. 2021Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibrationcited by 92
  31. 2021Graph Neural Networks in Network Neurosciencecited by 91
  32. 2023Edge Directionality Improves Learning on Heterophilic Graphscited by 89
  33. 2022PiFold: Toward effective and efficient protein inverse foldingcited by 83
  34. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  35. 2021Graph Structure Learning with Variational Information Bottleneckcited by 81
  36. 2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
  37. 2022DiffWire: Inductive Graph Rewiring via the Lov'asz Boundcited by 79
  38. 2021A Review on Graph Neural Network Methods in Financial Applicationscited by 78

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