Data to 7 Oct 2026

Scalable Deep Generative Modeling for Sparse Graphs

arXiv 2006.15502 ↗First posted 28 Jun 2020cs.LG stat.MLcited by 63

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Abstract (arXiv)

Learning graph generative models is a challenging task for deep learning and has wide applicability to a range of domains like chemistry, biology and social science. However current deep neural methods suffer from limited scalability: for a graph with n nodes and m edges, existing deep neural methods require Omega(n2) complexity by building up the adjacency matrix. On the other hand, many real world graphs are actually sparse in the sense that mll n2. Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to O((n + m)log n). Furthermore, during training this autoregressive model can be parallelized with O(log n) synchronization stages, which makes it much more efficient than other autoregressive models that require Omega(n). Experiments on several benchmarks show that the proposed approach not only scales to orders of magnitude larger graphs than previously possible with deep autoregressive graph generative models, but also yields better graph generation quality.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2020.

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Citing papers per year: 2021: 9; 2022: 13; 2023: 19; 2024: 6; 2025: 10; 2026: 6.

63 papers.

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2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
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
2023Autoregressive Diffusion Model for Graph Generationcited by 82
2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modelingcited by 75
2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
2025Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generationcited by 57
2022On Evaluation Metrics for Graph Generative Modelscited by 44
2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
2021Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutionscited by 38
2023Efficient and Scalable Graph Generation through Iterative Local Expansioncited by 35

38 more

2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
2021Order Matters: Probabilistic Modeling of Node Sequence for Graph Generationcited by 32
2023Sparse Training of Discrete Diffusion Models for Graph Generationcited by 26
2021Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNscited by 22
2021Multiresolution Equivariant Graph Variational Autoencodercited by 20
2022NVDiff: Graph Generation through the Diffusion of Node Vectorscited by 18
2023Curvature Filtrations for Graph Generative Model Evaluationcited by 18
2024Generative Modelling of Structurally Constrained Graphscited by 17
2023Metric Space Magnitude for Evaluating the Diversity of Latent Representationscited by 16
2025Graph Generative Pre-trained Transformercited by 16
2023Improving Graph Generation by Restricting Graph Bandwidthcited by 15
2023A Simple and Scalable Representation for Graph Generationcited by 15
2025Flatten Graphs as Sequences: Transformers are Scalable Graph Generatorscited by 12
2024Continual Learning on Graphs: A Surveycited by 11
2024LLM-Based Multi-Agent Systems are Scalable Graph Generative Modelscited by 11
2021Partition and Code: learning how to compress graphscited by 10
2023Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesiscited by 9
2023D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusioncited by 9
2022Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoderscited by 8
2025Bures-Wasserstein Flow Matching for Graph Generationcited by 7
2022Evaluating Graph Generative Models with Contrastively Learned Featurescited by 5
2023HiGen: Hierarchical Graph Generative Networkscited by 5
2021Learning Graphon Autoencoders for Generative Graph Modelingcited by 4
2023ARTree: A Deep Autoregressive Model for Phylogenetic Inferencecited by 4
2021TD-GEN: Graph Generation With Tree Decompositioncited by 3
2022AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generatorscited by 3
2022GEMS: Scene Expansion using Generative Models of Graphscited by 3
2023W2SAT: Learning to generate SAT instances from Weighted Literal Incidence Graphscited by 3
2023EDGE++: Improved Training and Sampling of EDGEcited by 3
2024Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussionscited by 3
2025Backdoor Attacks on Discrete Graph Diffusion Modelscited by 3
2021In-game Residential Home Planning via Visual Context-aware Global Relation Learningcited by 2
2022Large-Scale Auto-Regressive Modeling Of Street Networkscited by 2
2023Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturingcited by 2
2024Random Walk Diffusion for Efficient Large-Scale Graph Generationcited by 2
2025GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learningcited by 2
2025Scalable Generative Modeling of Weighted Graphscited by 2
2026Flowette: Flow Matching with Graphette Priors for Graph Generationcited by 2

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