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Deep Learning on Graphs: A Survey

arXiv 1812.04202 ↗First posted 11 Dec 2018cs.LG cs.SI stat.MLcited by 393

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Abstract (arXiv)

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.

Key facts. Cited by 393 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 110; 2022: 109; 2023: 74; 2024: 55; 2025: 31; 2026: 14.

393 papers.

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  1. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  2. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 361
  3. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  4. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  5. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  6. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  7. 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 145
  8. 2021Graph Learning: A Surveycited by 140
  9. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  10. 2022Disentangled Representation Learningcited by 111
  11. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  12. 2021BRepNet: A topological message passing system for solid modelscited by 92
38 more
  1. 2021Graph Neural Networks in Network Neurosciencecited by 91
  2. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  3. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  4. 2022PiFold: Toward effective and efficient protein inverse foldingcited by 82
  5. 2021Graph Representation Learning in Biomedicinecited by 79
  6. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  7. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  8. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  9. 2022Graph Rationalization with Environment-based Augmentationscited by 70
  10. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  11. 2021FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learningcited by 68
  12. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  13. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
  14. 2022A Survey on Graph Representation Learning Methodscited by 66
  15. 2021GraphSVX: Shapley Value Explanations for Graph Neural Networkscited by 63
  16. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  17. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  18. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  19. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  20. 2022Instance Relation Graph Guided Source-Free Domain Adaptive Object Detectioncited by 55
  21. 2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50
  22. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  23. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  24. 2021AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filtercited by 43
  25. 2022Task-Adaptive Few-shot Node Classificationcited by 43
  26. 2022Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structurecited by 43
  27. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  28. 2023Towards Understanding the Generalization of Graph Neural Networkscited by 41
  29. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  30. 2022AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBcited by 39
  31. 2022Linkless Link Prediction via Relational Distillationcited by 39
  32. 2021Automated Machine Learning on Graphs: A Surveycited by 38
  33. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  34. 2021ECLARE: Extreme Classification with Label Graph Correlationscited by 38
  35. 2022Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shiftcited by 38
  36. 2021Link Prediction with Persistent Homology: An Interactive Viewcited by 37
  37. 2021Spectral Temporal Graph Neural Network for Trajectory Predictioncited by 36
  38. 2021Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphscited by 35

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