Data to 7 Oct 2026

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

arXiv 1802.08773 ↗First posted 24 Feb 2018cs.LG cs.AI cs.SIcited by 406

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Cited by 72 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far. In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.

Key facts. Cited by 406 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 67; 2022: 75; 2023: 75; 2024: 72; 2025: 70; 2026: 46.

406 papers.

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2021E(n) Equivariant Graph Neural Networkscited by 951
2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 263
2021Identity-aware Graph Neural Networkscited by 197
2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
2022Graph neural networks for materials science and chemistrycited by 181
2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 177
2021E(n) Equivariant Normalizing Flowscited by 165
2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144

38 more

2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 137
2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Priorcited by 110
2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
2022Generating 3D Molecules for Target Protein Bindingcited by 104
2021Deep Generative Models in Engineering Design: A Reviewcited by 103
2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 96
2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
2021SceneGen: Learning to Generate Realistic Traffic Scenescited by 83
2023Autoregressive Diffusion Model for Graph Generationcited by 82
2021Graph Representation Learning in Biomedicinecited by 79
2024Variational Flow Matching for Graph Generationcited by 78
2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modelingcited by 75
2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 73
2021Directed Acyclic Graph Neural Networkscited by 70
2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
2022Diffusion Models for Graphs Benefit From Discrete State Spacescited by 69
2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modelingcited by 63
2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
2025Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generationcited by 57
2021Graph Coarsening with Neural Networkscited by 55
2023CktGNN: Circuit Graph Neural Network for Electronic Design Automationcited by 55
2023Graph Generation with Diffusion Mixturecited by 52
2022CLEAR: Generative Counterfactual Explanations on Graphscited by 48
2023Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generationcited by 46
2022On Evaluation Metrics for Graph Generative Modelscited by 44
2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
2021HDMapGen: A Hierarchical Graph Generative Model of High Definition Mapscited by 40
2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
2022Linkless Link Prediction via Relational Distillationcited by 39
2021Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutionscited by 38
2022Fast Graph Generation via Spectral Diffusioncited by 36
2023SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimizationcited by 35
2023Efficient and Scalable Graph Generation through Iterative Local Expansioncited by 35
2025AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologiescited by 35
2022Multi-modal Graph Learning for Disease Predictioncited by 34
2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34

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