mlsanityData to 7 Oct 2026

Inductive Representation Learning on Large Graphs

arXiv 1706.02216 ↗First posted 7 Jun 2017cs.SI cs.LG stat.MLcited by 6,284

Not on the map. Why?

Cited by 45 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

Key facts. Cited by 6,284 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2017.

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Citing papers per year: 2020: 2; 2021: 925; 2022: 1070; 2023: 1152; 2024: 1220; 2025: 1109; 2026: 806.

6,284 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2021How Attentive are Graph Attention Networks?cited by 818
  3. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  4. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  5. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  6. 2021Neural Operator: Learning Maps Between Function Spacescited by 413
  7. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 361
  8. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  9. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  10. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  11. 2024Graph Retrieval-Augmented Generation: A Surveycited by 320
  12. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
38 more
  1. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  2. 2024Transolver: A Fast Transformer Solver for PDEs on General Geometriescited by 299
  3. 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEscited by 293
  4. 2021Graph Self-Supervised Learning: A Surveycited by 289
  5. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  6. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  7. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  8. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  9. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 263
  10. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  11. 2021GRAND: Graph Neural Diffusioncited by 258
  12. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  13. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 254
  14. 2021A Survey on Deep Semi-supervised Learningcited by 250
  15. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  16. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 246
  17. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  18. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  19. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223
  20. 2023Language is All a Graph Needscited by 219
  21. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
  22. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  23. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  24. 2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 203
  25. 2023DepGraph: Towards Any Structural Pruningcited by 202
  26. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  27. 2021Identity-aware Graph Neural Networkscited by 197
  28. 2022Structure-Aware Transformer for Graph Representation Learningcited by 191
  29. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 190
  30. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
  31. 2022Discovering Invariant Rationales for Graph Neural Networkscited by 186
  32. 2022Rethinking Graph Neural Networks for Anomaly Detectioncited by 185
  33. 2022Vision GNN: An Image is Worth Graph of Nodescited by 185
  34. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  35. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  36. 2021Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networkscited by 182
  37. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  38. 2022Graph neural networks for materials science and chemistrycited by 181

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