mlsanityData to 7 Oct 2026

Efficient Graph Generation with Graph Recurrent Attention Networks

arXiv 1910.00760 ↗First posted 2 Oct 2019cs.LG stat.MLcited by 231

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Cited by 53 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nodes and associated edges at a time. The block size and sampling stride allow us to trade off sample quality for efficiency. Compared to previous RNN-based graph generative models, our framework better captures the auto-regressive conditioning between the already-generated and to-be-generated parts of the graph using Graph Neural Networks (GNNs) with attention. This not only reduces the dependency on node ordering but also bypasses the long-term bottleneck caused by the sequential nature of RNNs. Moreover, we parameterize the output distribution per block using a mixture of Bernoulli, which captures the correlations among generated edges within the block. Finally, we propose to handle node orderings in generation by marginalizing over a family of canonical orderings. On standard benchmarks, we achieve state-of-the-art time efficiency and sample quality compared to previous models. Additionally, we show our model is capable of generating large graphs of up to 5K nodes with good quality. To the best of our knowledge, GRAN is the first deep graph generative model that can scale to this size. Our code is released at: https://github.com/lrjconan/GRAN.

Key facts. Cited by 231 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2019.

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Citing papers per year: 2021: 46; 2022: 41; 2023: 48; 2024: 32; 2025: 36; 2026: 28.

231 papers.

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  11. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  12. 2021SceneGen: Learning to Generate Realistic Traffic Scenescited by 83
38 more
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  8. 2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
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  11. 2021Keeping it Simple: Language Models can learn Complex Molecular Distributionscited by 68
  12. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  13. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  14. 2025Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generationcited by 57
  15. 2021Graph Coarsening with Neural Networkscited by 55
  16. 2023CktGNN: Circuit Graph Neural Network for Electronic Design Automationcited by 55
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  21. 2021HDMapGen: A Hierarchical Graph Generative Model of High Definition Mapscited by 40
  22. 2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
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  25. 2023AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodiescited by 35
  26. 2023Efficient and Scalable Graph Generation through Iterative Local Expansioncited by 35
  27. 2022Multi-modal Graph Learning for Disease Predictioncited by 34
  28. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  29. 2021Order Matters: Probabilistic Modeling of Node Sequence for Graph Generationcited by 32
  30. 2024Cometh: A continuous-time discrete-state graph diffusion modelcited by 29
  31. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  32. 2023SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generationcited by 26
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  35. 2021Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNscited by 22
  36. 2021Unconditional Scene Graph Generationcited by 22
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