mlsanityData to 13 Oct 2026

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

arXiv 1802.03480 ↗First posted 9 Feb 2018cs.LG cs.CV cs.NEcited by 378

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Cited by 61 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of molecule generation.

Key facts. Cited by 378 papers in the map (data through 13 Oct 2026). First posted on arXiv 9 Feb 2018.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2020: 1; 2021: 61; 2022: 71; 2023: 72; 2024: 73; 2025: 66; 2026: 34.

378 papers.

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  1. 2021E(n) Equivariant Graph Neural Networkscited by 957
  2. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 672
  3. 2021Score-based Generative Modeling in Latent Spacecited by 635
  4. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 276
  5. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 266
  6. 2022Graph neural networks for materials science and chemistrycited by 181
  7. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 178
  8. 2021E(n) Equivariant Normalizing Flowscited by 165
  9. 2021Geometric Deep Learning on Molecular Representationscited by 113
  10. 2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Priorcited by 112
  11. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  12. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 98
38 more
  1. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  2. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  3. 2023Autoregressive Diffusion Model for Graph Generationcited by 82
  4. 2024Variational Flow Matching for Graph Generationcited by 82
  5. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  6. 2021Graph Representation Learning in Biomedicinecited by 80
  7. 2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modelingcited by 77
  8. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 75
  9. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 73
  10. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  11. 2022Diffusion Models for Graphs Benefit From Discrete State Spacescited by 71
  12. 2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
  13. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  14. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  15. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  16. 2023DrugAssist: A Large Language Model for Molecule Optimizationcited by 57
  17. 2021Graph Coarsening with Neural Networkscited by 55
  18. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  19. 2021Local Augmentation for Graph Neural Networkscited by 53
  20. 2023Graph Generation with Diffusion Mixturecited by 53
  21. 2022CLEAR: Generative Counterfactual Explanations on Graphscited by 48
  22. 2023Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generationcited by 46
  23. 2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
  24. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  25. 2023Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Spacecited by 40
  26. 2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
  27. 2021Quantum Generative Models for Small Molecule Drug Discoverycited by 37
  28. 2021Top-N: Equivariant set and graph generation without exchangeabilitycited by 36
  29. 2022Fast Graph Generation via Spectral Diffusioncited by 36
  30. 2023Efficient and Scalable Graph Generation through Iterative Local Expansioncited by 36
  31. 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligencecited by 35
  32. 2022Multi-modal Graph Learning for Disease Predictioncited by 34
  33. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  34. 20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
  35. 2024LLMs generate structurally realistic social networks but overestimate political homophilycited by 32
  36. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
  37. 2025DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectracited by 30
  38. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28

50 most cited of 378 All 378 (JSON)