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Learning Convolutional Neural Networks for Graphs

arXiv 1605.05273 ↗First posted 17 May 2016cs.LG cs.AI stat.MLcited by 314

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Abstract (arXiv)

Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be undirected, directed, and with both discrete and continuous node and edge attributes. Analogous to image-based convolutional networks that operate on locally connected regions of the input, we present a general approach to extracting locally connected regions from graphs. Using established benchmark data sets, we demonstrate that the learned feature representations are competitive with state of the art graph kernels and that their computation is highly efficient.

Key facts. Cited by 314 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 109; 2022: 91; 2023: 52; 2024: 36; 2025: 22; 2026: 4.

314 papers.

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  1. 2021Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognitioncited by 317
  2. 2022Vision GNN: An Image is Worth Graph of Nodescited by 185
  3. 2022Graph neural networks for materials science and chemistrycited by 181
  4. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  5. 2021Frame Averaging for Invariant and Equivariant Network Designcited by 166
  6. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  7. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  8. 2021Graph Learning: A Surveycited by 140
  9. 2021A Comprehensive Survey of Scene Graphs: Generation and Applicationcited by 125
  10. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  11. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  12. 2021BRepNet: A topological message passing system for solid modelscited by 92
38 more
  1. 2022Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 85
  2. 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationcited by 83
  3. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  4. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  5. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  6. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  7. 2022Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognitioncited by 71
  8. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  9. 2021Lorentzian Graph Convolutional Networkscited by 66
  10. 2022A Survey on Graph Representation Learning Methodscited by 66
  11. 2021Reconstruction for Powerful Graph Representationscited by 65
  12. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  13. 2021Temporal Knowledge Graph Forecasting with Neural ODEcited by 61
  14. 2023Graph Prompt Learning: A Comprehensive Survey and Beyondcited by 61
  15. 2021A Survey on Embedding Dynamic Graphscited by 60
  16. 2022Ordered Subgraph Aggregation Networkscited by 57
  17. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  18. 2023CktGNN: Circuit Graph Neural Network for Electronic Design Automationcited by 55
  19. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  20. 2021Local Augmentation for Graph Neural Networkscited by 52
  21. 2021Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passingcited by 46
  22. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  23. 2021Dynamic Network Embedding Surveycited by 44
  24. 2021AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filtercited by 43
  25. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  26. 2021p-Laplacian Based Graph Neural Networkscited by 39
  27. 2021Online Graph Dictionary Learningcited by 37
  28. 2021Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervisioncited by 37
  29. 2021Edge Representation Learning with Hypergraphscited by 37
  30. 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
  31. 2021Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Predictioncited by 36
  32. 20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
  33. 2021Conditional Local Convolution for Spatio-temporal Meteorological Forecastingcited by 30
  34. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  35. 2022Skeleton-Parted Graph Scattering Networks for 3D Human Motion Predictioncited by 29
  36. 2021Learning Conjoint Attentions for Graph Neural Netscited by 26
  37. 2021Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecturecited by 25
  38. 2023Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Datacited by 25

50 most cited of 314 All 314 (JSON)