mlsanityData to 14 Oct 2026

Revisiting Semi-Supervised Learning with Graph Embeddings

arXiv 1603.08861 ↗First posted 29 Mar 2016cs.LGcited by 976

Not on the map. Why?

Cited by 29 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.

Key facts. Cited by 976 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 136; 2022: 133; 2023: 174; 2024: 213; 2025: 185; 2026: 135.

976 papers.

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  1. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  2. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
  3. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 329
  4. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  5. 2021Graph Self-Supervised Learning: A Surveycited by 289
  6. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  7. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 243
  8. 2023Language is All a Graph Needscited by 220
  9. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  10. 2022Relative representations enable zero-shot latent space communicationcited by 191
  11. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  12. 2024LLaGA: Large Language and Graph Assistantcited by 181
38 more
  1. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  2. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  3. 2022Handling Distribution Shifts on Graphs: An Invariance Perspectivecited by 164
  4. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  5. 2021Graph Learning: A Surveycited by 141
  6. 2021Graph Unlearningcited by 138
  7. 2023Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learningcited by 133
  8. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 132
  9. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  10. 2023Similarity of Neural Network Models: A Survey of Functional and Representational Measurescited by 131
  11. 2022Transformer for Graphs: An Overview from Architecture Perspectivecited by 107
  12. 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
  13. 2021InfoGCL: Information-Aware Graph Contrastive Learningcited by 105
  14. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  15. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 98
  16. 2021Dirichlet Energy Constrained Learning for Deep Graph Neural Networkscited by 97
  17. 2022Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networkscited by 94
  18. 2021GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddingscited by 90
  19. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  20. 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusioncited by 87
  21. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 85
  22. 2024ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphscited by 85
  23. 2021Beltrami Flow and Neural Diffusion on Graphscited by 84
  24. 2021GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophilycited by 84
  25. 2021New Benchmarks for Learning on Non-Homophilous Graphscited by 81
  26. 2021Scaling Up Graph Neural Networks Via Graph Coarseningcited by 80
  27. 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
  28. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  29. 2023SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learningcited by 73
  30. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  31. 2021Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learningcited by 70
  32. 2021Learning from Counterfactual Links for Link Predictioncited by 69
  33. 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
  34. 2022Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminationcited by 69
  35. 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarkingcited by 69
  36. 2021Lorentzian Graph Convolutional Networkscited by 68
  37. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 67
  38. 2023Neural Common Neighbor with Completion for Link Predictioncited by 64

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