mlsanityData to 14 Oct 2026

Graph Normalizing Flows

arXiv 1905.13177 ↗First posted 30 May 2019cs.LG stat.MLcited by 104

Not on the map. Why?

Cited by 23 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the unsupervised case, we combine graph normalizing flows with a novel graph auto-encoder to create a generative model of graph structures. Our model is permutation-invariant, generating entire graphs with a single feed-forward pass, and achieves competitive results with the state-of-the art auto-regressive models, while being better suited to parallel computing architectures.

Key facts. Cited by 104 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 15; 2022: 23; 2023: 21; 2024: 17; 2025: 15; 2026: 12.

104 papers.

Sort:

By area:

  1. 2021E(n) Equivariant Graph Neural Networkscited by 965
  2. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 283
  3. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 270
  4. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  5. 2021E(n) Equivariant Normalizing Flowscited by 165
  6. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 110
  7. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 99
  8. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
  9. 2024Variational Flow Matching for Graph Generationcited by 83
  10. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  11. 2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modelingcited by 80
  12. 2022Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Predictioncited by 78
38 more
  1. 2022Diffusion Models for Graphs Benefit From Discrete State Spacescited by 73
  2. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 60
  3. 2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
  4. 2022Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Seriescited by 42
  5. 2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 40
  6. 2022Normalizing flow neural networks by JKO schemecited by 39
  7. 2022Fast Graph Generation via Spectral Diffusioncited by 36
  8. 2023Efficient and Scalable Graph Generation through Iterative Local Expansioncited by 36
  9. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 35
  10. 2023SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generationcited by 28
  11. 2023Symmetry Breaking and Equivariant Neural Networkscited by 25
  12. 2021Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learningcited by 23
  13. 2023Data-centric Graph Learning: A Surveycited by 22
  14. 2022Graph Neural Networks for Moleculescited by 21
  15. 2021Multiresolution Equivariant Graph Variational Autoencodercited by 20
  16. 2024Generative Modelling of Structurally Constrained Graphscited by 19
  17. 2022Target-aware Molecular Graph Generationcited by 18
  18. 2022First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Datacited by 18
  19. 2022NVDiff: Graph Generation through the Diffusion of Node Vectorscited by 18
  20. 2021PU-Flow: a Point Cloud Upsampling Network with Normalizing Flowscited by 17
  21. 2025Graph Generative Pre-trained Transformercited by 17
  22. 2023A Simple and Scalable Representation for Graph Generationcited by 16
  23. 2025Improving Equivariant Networks with Probabilistic Symmetry Breakingcited by 16
  24. 2023Sample Complexity of Probability Divergences under Group Symmetrycited by 14
  25. 2021Learning Augmentation Distributions using Transformed Risk Minimizationcited by 13
  26. 2024Graph Diffusion Policy Optimizationcited by 13
  27. 2024Advancing Graph Generation through Beta Diffusioncited by 13
  28. 2021Graph-based Normalizing Flow for Human Motion Generation and Reconstructioncited by 12
  29. 2022Semi-Discrete Normalizing Flows through Differentiable Tessellationcited by 11
  30. 2024LLM-Based Multi-Agent Systems are Scalable Graph Generative Modelscited by 11
  31. 2023Discrete Graph Auto-Encodercited by 10
  32. 2025ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitalscited by 10
  33. 2025Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generationcited by 10
  34. 2023E(n)-equivariant Graph Neural Cellular Automatacited by 9
  35. 2023Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesiscited by 9
  36. 2024Equivariant Symmetry Breaking Setscited by 9
  37. 2024Physics-Informed Regularization for Domain-Agnostic Dynamical System Modelingcited by 9
  38. 2024GLAD: Improving Latent Graph Generative Modeling with Simple Quantizationcited by 8

50 most cited of 104 All 104 (JSON)