mlsanityData to 7 Oct 2026

Variational Graph Auto-Encoders

arXiv 1611.07308 ↗First posted 21 Nov 2016stat.ML cs.LGcited by 1,373

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Cited by 38 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.

Key facts. Cited by 1,373 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 234; 2022: 266; 2023: 285; 2024: 293; 2025: 199; 2026: 96.

1,373 papers.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2021E(n) Equivariant Graph Neural Networkscited by 951
  3. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  4. 2021Graph Self-Supervised Learning: A Surveycited by 289
  5. 2021Graph Contrastive Learning Automatedcited by 287
  6. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  7. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  8. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  9. 2021A Survey on Deep Semi-supervised Learningcited by 250
  10. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  11. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  12. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
38 more
  1. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  2. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  3. 2022Graph neural networks for materials science and chemistrycited by 181
  4. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  5. 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
  6. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
  7. 2022Protein Representation Learning by Geometric Structure Pretrainingcited by 161
  8. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  9. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  10. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
  11. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 141
  12. 2021Graph Learning: A Surveycited by 140
  13. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
  14. 2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 137
  15. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  16. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  17. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 131
  18. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  19. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  20. 2021Self-supervised Graph-level Representation Learning with Local and Global Structurecited by 125
  21. 2021Generative Causal Explanations for Graph Neural Networkscited by 124
  22. 2022Pre-training via Denoising for Molecular Property Predictioncited by 124
  23. 2022Towards Unsupervised Deep Graph Structure Learningcited by 112
  24. 2024G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networkscited by 110
  25. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  26. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  27. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
  28. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  29. 2022Universal Prompt Tuning for Graph Neural Networkscited by 100
  30. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  31. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 96
  32. 2022Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networkscited by 94
  33. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  34. 2021Graph Barlow Twins: A self-supervised representation learning framework for graphscited by 85
  35. 2024Position: Graph Foundation Models are Already Herecited by 84
  36. 2024All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretrainingcited by 84
  37. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  38. 2022What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoderscited by 82

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