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Diffusion-Convolutional Neural Networks

arXiv 1511.02136 ↗First posted 6 Nov 2015cs.LGcited by 268

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Abstract (arXiv)

We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including a latent representation for graphical data that is invariant under isomorphism, as well as polynomial-time prediction and learning that can be represented as tensor operations and efficiently implemented on the GPU. Through several experiments with real structured datasets, we demonstrate that DCNNs are able to outperform probabilistic relational models and kernel-on-graph methods at relational node classification tasks.

Key facts. Cited by 268 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 75; 2022: 74; 2023: 48; 2024: 41; 2025: 21; 2026: 9.

268 papers.

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  1. 2021How Attentive are Graph Attention Networks?cited by 818
  2. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  3. 2021GRAND: Graph Neural Diffusioncited by 258
  4. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  5. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  6. 2022Vision GNN: An Image is Worth Graph of Nodescited by 185
  7. 2022DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Modelscited by 167
  8. 2021Equivariant Subgraph Aggregation Networkscited by 154
  9. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  10. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  11. 2021Graph Learning: A Surveycited by 140
  12. 2021Graph Unlearningcited by 129
38 more
  1. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  2. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  3. 2022Understanding and Extending Subgraph GNNs by Rethinking Their Symmetriescited by 112
  4. 2021MagNet: A Neural Network for Directed Graphscited by 104
  5. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  6. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  7. 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusioncited by 85
  8. 2021Space-Time-Separable Graph Convolutional Network for Pose Forecastingcited by 84
  9. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  10. 2021Understanding Pooling in Graph Neural Networkscited by 69
  11. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  12. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  13. 2023CktGNN: Circuit Graph Neural Network for Electronic Design Automationcited by 55
  14. 2021Conditional Directed Graph Convolution for 3D Human Pose Estimationcited by 52
  15. 2021Local Augmentation for Graph Neural Networkscited by 52
  16. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  17. 2021AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filtercited by 43
  18. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  19. 2022Simplicial Attention Networkscited by 40
  20. 2021VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantizationcited by 39
  21. 2021p-Laplacian Based Graph Neural Networkscited by 39
  22. 2021Edge Representation Learning with Hypergraphscited by 37
  23. 2022Cell Attention Networkscited by 34
  24. 2022Explaining the Explainers in Graph Neural Networks: a Comparative Studycited by 34
  25. 2021Conditional Local Convolution for Spatio-temporal Meteorological Forecastingcited by 30
  26. 2023Kriging Convolutional Networkscited by 30
  27. 2022DropMessage: Unifying Random Dropping for Graph Neural Networkscited by 29
  28. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  29. 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
  30. 2022Conditional Generation Net for Medication Recommendationcited by 28
  31. 2022Optimization-Induced Graph Implicit Nonlinear Diffusioncited by 28
  32. 2022RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Networkcited by 27
  33. 2021Learning Conjoint Attentions for Graph Neural Netscited by 26
  34. 2022Higher-order Clustering and Pooling for Graph Neural Networkscited by 26
  35. 2021A New Perspective on the Effects of Spectrum in Graph Neural Networkscited by 24
  36. 2022Graph Attention Retrospectivecited by 24
  37. 2022Exploring Edge Disentanglement for Node Classificationcited by 23
  38. 2021Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networkscited by 22

50 most cited of 268 All 268 (JSON)