mlsanityData to 14 Oct 2026

Semi-Supervised Classification with Graph Convolutional Networks

arXiv 1609.02907 ↗First posted 9 Sep 2016cs.LG stat.MLcited by 11,361

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Cited by 102 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Key facts. Cited by 11,361 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Sep 2016.

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Citing papers per year: 2020: 2; 2021: 1858; 2022: 1996; 2023: 2159; 2024: 2175; 2025: 1820; 2026: 1351.

11,361 papers.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  2. 2021Transformers in Vision: A Surveycited by 1,013
  3. 2021E(n) Equivariant Graph Neural Networkscited by 965
  4. 2021How Attentive are Graph Attention Networks?cited by 835
  5. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 713
  6. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 478
  7. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  8. 2021Rethinking Graph Transformers with Spectral Attentioncited by 473
  9. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  10. 2021Neural Operator: Learning Maps Between Function Spacescited by 414
  11. 2022Message Passing Neural PDE Solverscited by 386
  12. 2021A Survey on Automated Fact-Checkingcited by 378
38 more
  1. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 362
  2. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
  3. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 333
  4. 2021AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecastingcited by 331
  5. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 329
  6. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  7. 2021Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognitioncited by 322
  8. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  9. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 320
  10. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  11. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  12. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  13. 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEscited by 303
  14. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  15. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  16. 2021Graph Self-Supervised Learning: A Surveycited by 289
  17. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  18. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 279
  19. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 275
  20. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  21. 20213D Human Pose Estimation with Spatial and Temporal Transformerscited by 272
  22. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 270
  23. 2021Combinatorial optimization and reasoning with graph neural networkscited by 268
  24. 2021Mesh Graphormercited by 265
  25. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 264
  26. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 264
  27. 2022Long Range Graph Benchmarkcited by 263
  28. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  29. 2021GRAND: Graph Neural Diffusioncited by 258
  30. 2021A Survey on Deep Semi-supervised Learningcited by 251
  31. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 247
  32. 2022Transformers in Medical Imaging: A Surveycited by 245
  33. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 243
  34. 2022Diffusion models as plug-and-play priorscited by 231
  35. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  36. 2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 228
  37. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 226
  38. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223

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