We welcome researchers to share related work to enrich this list or provide insightful comments on our survey. Feel free to reach out to the corresponding co-first authors: Jiarui Ji, Wenda Wang.
The models in our survey paper are available at notion table Graph Generative Models Collection. Contributions and edits are welcome.
- (ICML 2018) GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models GraphRNN
- (arXiv 2019) MolecularRNN: Generating realistic molecular graphs with optimized properties MolecularRNN
- (chemical science 2019) A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space GraphMCTS
- (NIPS 2019) Efficient Graph Generation with Graph Recurrent Attention Networks GRAN
- (JCIM 2020) Bidirectional Molecule Generation with Recurrent Neural Networks BIMODAL
- (ICLR 2020) GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation GraphAF
- (ICML 2020) Scalable Deep Generative Modeling for Sparse Graphs BiGG
- (WWW 2020) GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation GraphGen
- (molecular informatics 2021) MGRNN: Structure generation of molecules based on graph recurrent neural networks MGRNN
- (Neurocomputing 2021) Molecular generative Graph Neural Networks for Drug Discovery MG2N2
- (NC 2021) Masked graph modeling for molecule generation MGM
- (IJCNN 2021) Graphgen-redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation GraphGen-Redux
- (SIAM 2021) Disentangled Dynamic Graph Deep Generation D2G2
- (AAAI 2022) TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs TIGGER
- (IJMS 2023) Graphgpt: A graph enhanced generative pretrained transformer for conditioned molecular generation GraphGPT-1
- (arxiv 2024) TransFlower: An Explainable Transformer-Based Model with Flow-to-Flow Attention for Commuting Flow Prediction TransFlower
- (arXiv 2024) Exploring the Potential of Large Language Models in Graph Generation LLM4GraphGen
- (NMI 2024) Generation of 3D molecules in pockets via a language model Lingo3DMol
- (TMLR 2024) Overcoming Order in Autoregressive Graph Generation for Molecule Generation OLR
- (Computers and Chemical Engineering 2024) Leveraging 2D molecular graph pretraining for improved 3D conformer generation with graph neural networks GeoMol
- (ICLR 2024) A Simple and Scalable Representation for Graph Generation GEEL
- (arxiv 2024) GraphGPT: Graph Learning with Generative Pre-trained Transformers GraphGPT-2
- (AAAI 2025) BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning A BindGPT
- (arXiv 2025) Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens Uni-3Dar
- (TOG 2016) A deep learning framework for character motion synthesis and editing motion-framework
- (NIPS 2016) Variational Graph Auto-Encoders VGAE
- (ACS Central Science 2018) Automatic chemical design using a data-driven continuous representation of molecules CharVAE
- (cheminformatics 2018) Molecular generative model based on conditional variational autoencoder for de novo molecular design CVAE
- (ICML 2018) Junction Tree Variational Autoencoder for Molecular Graph Generation JT-VAE
- (NIPS 2018) Constrained Graph Variational Autoencoders for Molecule Design CGVAE
- (ICANN 2018) GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders GraphVAE
- (AAAI 2019) NeVAE: A Deep Generative Model for Molecular Graphs A NeVAE
- (ICLR 2019) Learning Multimodal Graph-to-Graph Translation for Molecular Optimization VJTNN
- (NIPS 2019) Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules G-Schnet
- (ICML 2019) Graphite: Iterative Generative Modeling of Graphs Graphite
- (PAKDD 2020) Attention-Based Graph Evolution AGE
- (KDD 2020) GPT-GNN: Generative Pre-Training of Graph Neural Networks GPT-GNN
- (KDD 2022) GraphMAE: Self-Supervised Masked Graph Autoencoders GraphMAE
- (ICLR 2023) De Novo Molecular Generation via Connection-aware Motif Mining MiCaM
- (SIAM 2023) MoVAE: A Variational AutoEncoder for Molecular Graph Generation MoVAE
- (ICDE 2024) Efficient Dynamic Attributed Graph Generation VRDAG
- (AAAI 2025) A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation DGGEN
- (arXiv 2017) Objective-Reinforced Generative Adversarial Networks (ORGAN) ORGAN
- (ICML 2018) MolGAN: An implicit generative model for small molecular graphs MolGAN
- (NIPS 2018) Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation GCPN
- (AAAI 2018) GraphGAN: Graph Representation Learning With Generative Adversarial Nets GraphGAN
- (ICML 2018) NetGAN: Generating Graphs via Random Walks NetGAN
- (NIPS 2019) Conditional Structure Generation through Graph Variational Generative Adversarial Nets CONDGEN
- ( ECML PKDD 2020) Adversarial Learned Molecular Graph Inference and Generation ALMGIG
- (WWW 2020) TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models TG-GAN
- (KDD 2020) A Data-Driven Graph Generative Model for Temporal Interaction Networks TagGen
- (ecml PKDD 2022) STGEN: Deep Continuous-Time Spatiotemporal Graph Generation STGEN
- (arxiv 2023) Molecular Graph Generation by Decomposition and Reassembling MOLDR
- (chemical science 2024) FragGen: towards 3D geometry reliable fragment-based molecular generation FragGen
- (JCIM 2025) Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation Mol-AIR
- (AISTATS 2020) Permutation Invariant Graph Generation via Score-Based Generative Modeling EDP-GNN
- (TOG 2020) MoGlow: probabilistic and controllable motion synthesis using normalising flows MoGlow
- (ICLR 2021) GraphEBM: Molecular Graph Generation with Energy-Based Models GraphEBM
- (ICML 2022) Equivariant Diffusion for Molecule Generation in 3D EDM
- (ICML 2022) Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations GDSS
- (ICML 2023) Geometric latent diffusion models for 3d molecule generation GeoLDM
- (ICLR 2023) Equivariant Energy-Guided SDE EEGSDE
- (AAAI 2023) Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation CDGS
- (ICML 2023) Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling EDGE
- (arxiv 2023) Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model DiffODGen
- (Bioinform. 2023) DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces DeepRank-GNN
- (ICLR 2023) Digress: Discrete Denoising diffusion for graph generation Digress
- (NIPS 2024) Conditional Synthesis of 3D Molecules with Time Correction Sampler TACS
- (NIPS 2024) FairWire: Fair Graph Generation FairWire
- (TMLR 2024) GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? GraphMaker
- (Tpami 2024) MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model MotionDiffuse
- (TPAMI 2024) MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model MotionDiffuse
- (ICLR 2025) NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation Next-Mol
- (ICLR 2025) A Large-scale Training Paradigm for Graph Generative Models LGGM
- (ICLR 2025) Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency THePUff
- (arXiv 2019) Graphnvp: An invertible flow model for generating molecular graphs Graphnvp
- (KDD 2020) MoFlow: An Invertible Flow Model for Generating Molecular Graphs MoFlow
- (NIPS 2021) E(n) Equivariant Normalizing Flows ENF
- (ICLR 2021) Categorical Normalizing Flows via Continuous Transformations GraphCNF
- (AAAI 2021) MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation MolGrow
- (ICML 2021) GraphDF: A Discrete Flow Model for Molecular Graph Generation GraphDF
- (ICLR 2021) Learning Neural Generative Dynamics for Molecular Conformation Generation CGCF
- (ELLIS workshop 2022) FastFlows: Flow-Based Models for Molecular Graph Generation FastFlows
- (ICLR 2022) An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch G-SphereNet
- (CIKM 2022) DEMO: DeMo
- (NIPS 2023) Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation EquiFM
- (NIPS 2024) Variational Flow Matching for Graph Generation CatFlow
- (NIPS 2024) Navigating Chemical Space with Latent Flows ChemFlow
- (NIPS 2024) ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation ETFlow
- (arXiv 2024) Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation FlowMol
- (ICLR 2024) Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks GeoBFN
- (NIPS 2024) Improving Molecular Graph Generation with Flow Matching and Optimal Transport GGFlow
- (ICLR 2025) Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport GOAT
- (arXiv 2025) Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation A-GFN
- ( AISTATS 2025) Semlaflow--efficient 3d molecular generation with latent attention and equivariant flow matching Semlaflow
- (Publicationes Mathematicae 1960) On the evolution of random graphs ER
- (science 1999) Emergence of scaling in random networks BA
- (Social networks 2007) An introduction to exponential random graph (p*) models for social networks ERGM
- (Stoc 2009) Affiliation networks stoc_affiliation
- (JMLR 2010) Kronecker Graphs: An Approach to Modeling Networks FastKronecker
- (Scientific reports 2012) Activity driven modeling of time varying networks AND
- (SIGMOD 2016) ROLL:Fast In-Memory Generation of Gigantic Scale-free Networks ROLL-Tree
- (SIGMOD 2017) TrillionG: A Trillion-scale Synthetic Graph Generator using a Recursive Vector Model TrillionG
- (ICDE 2021) FastSGG: Efficient Social Graph Generation Using a Degree Distribution Generation Model FastSGG
- (WWW 2021) DYMOND: DYnamic MOtif-NoDes Network Generative Model DYMOND
- (Information Systems 2023) GenCAT: Generating attributed graphs with controlled relationships between classes, attributes, and topology GenCAT
- (UIST 2022) Social Simulacra: Creating Populated Prototypes for Social Computing Systems Social_simulacra
- (Arxiv 2023) S3: Social-network Simulation System with Large Language Model-Empowered Agents S3
- (ECAI 2024) SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems SUBER
- (WWW 2024) AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems AgentCF
- (EMNLP 2024) BASES: Large-scale Web Search User Simulation with Large Language Model based Agents BASES
- (EMNLP 2024) SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent SRAP-Agent
- (Arxiv 2024) The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents FUSE
- (Arxiv 2024) A Large-scale Time-aware Agents Simulation for Influencer Selection in Digital Advertising Campaigns TIS
- (Arxiv 2024) OASIS: Open Agent Social Interaction Simulations with One Million Agents OASIS
- (Arxiv 2024) Y Social: an LLM-powered Social Media Digital Twin Y_social
- (TOIS 2025) User Behavior Simulation with Large Language Model-based Agents RecAgent
- (ACL 2025) LLM-Based Multi-Agent Systems are Scalable Graph Generative Models GAG
- (WWW 2025) RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems RecUserSim
- (Massive graph analytics 2022) Recent advances in scalable network generation Paper
- (CSUR 2019) Random graph modeling: A survey of the concepts Paper
- (CSUR 2020) Graph Generators: State of the Art and Open Challenges Paper
- (CSUR 2025) A Survey on Hypergraph Mining: Patterns, Tools, and Generators Paper
- (IEEE Access, 2021) Deep Graph Generators: A Survey Paper
- (LOG 2022) A Survey on Deep Graph Generation: Methods and Applications Paper
- (IEEE Access 2023) Deep Generative Models for Synthetic Data: A Survey Paper
- (IJCAI 2023) Deep Generative Models for Synthetic Data: A Survey Paper
- (Arxiv 2024) From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents Paper