- A curated list of up-to-date graph generation papers and resources.
- This Repo is being actively updated and maintained! 03/17/24
- Please let us know if we miss any papers!
Machine Learning-Aided Generative Molecular Design Nature Machine Intelligence 2024
Generative Diffusion Models on Graphs: Methods and Applications IJCAI 2023
A Survey on Deep Graph Generation: Methods and Applications LoG 2022
Deep graph generators: A survey IEEE Access 2021
Graph Generators: State of the art and open challenges CSUR 2020
A Systematic Survey on Deep Generative Models for Graph Generation TPAMI 2022
GraphGT: Machine Learning Datasets for Graph Generation and Transformation NeurIPS 2021
Variational Flow Matching for Graph Generation NeurIPS 2024
Doob’s Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling NeurIPS 2024
Navigating Chemical Space with Latent Flows NeurIPS 2024
DeFoG: Discrete Flow Matching for Graph Generation Arxiv 2024
Fisher Flow Matching for Generative Modeling over Discrete Data Arxiv 2024
Efficient and Scalable Graph Generation through Iterative Local Expansion ICLR 2024
Sparse Training of Discrete Diffusion Models for Graph Generation Arxiv 2024
Equivariant flow matching NeurIPS 2023
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation NeurIPS 2023
Autoregressive Diffusion Model for Graph Generation ICML 2023
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation IJCAI 2023
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling ICML 2023
Graph Generative Model for Benchmarking Graph Neural Networks ICML 2023
DiGress: Discrete Denoising diffusion for graph generation ICLR 2023
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation AAAI 2023
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation ICDM 2022
Disentangled Spatiotemporal Graph Generative Models AAAI 2022
Deep Generative Model for Periodic Graphs NeurIPS 2022
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators NeurIPS 2022
Evaluating Graph Generative Models with Contrastively Learned Features NeurIPS 2022
Symmetry-induced Disentanglement on Graphs NeurIPS 2022
An Unpooling Layer for Graph Generation Arxiv 2022
Deep graph translation TNNLS 2022
Top-N: Equivariant Set and Graph Generation without Exchangeability ICLR 2022
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations ICML 2022
TD-GEN: Graph Generation Using Tree Decomposition AISTATS 2022
Deep generative models for spatial networks SIGKDD 2021
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation ICML 2021
GraphDF: A discrete flow model for molecular graph generation ICML 2021
Interpretable deep graph generation with node-edge co-disentanglement SIGKDD 2020
Multi-MotifGAN (MMGAN): Motif-Targeted graph generation and prediction ICASSP 2020
Node-edge co-disentangled representation learning for attributed graph generation SIGKDD 2020
Unsupervised joint k-node graph representations with compositional energy-based models NeurIPS 2020
Scalable Deep Generative Modeling for Sparse Graphs ICML 2020
Network principled deep generative models for designing drug combinations as graph sets Bioinformatics 2020
Permutation invariant graph generation via score-Based generative modeling AISTATS 2020
GraphGen: a scalable approach to domain-agnostic labeled graph generation WWW 2020
Edge-based sequential graph generation with recurrent neural networks Neurocomputing 2020
Graph deconvolutional generation Arxiv 2020
Efficient graph generation with graph recurrent attention networks NeurIPS 2019
Graphite: Iterative Generative Modeling of Graphs ICML 2019
Deep Q-Learning for directed acyclic graph generation ICML 2019 Workshop
Decoding molecular graph embeddings with reinforcement learning ICML 2019 Workshop
STGGAN: Spatial-temporal graph generation SIGSPATIAL 2019
Gravity-inspired graph autoencoders for directed link prediction CIKM 2019
Can NetGAN be improved on short random walks? BRACIS 2019
D-vae: A variational autoencoder for directed acyclic graphs NeurIPS 2019
Graph normalizing flows NeurIPS 2019
Graph generation by sequential edge prediction ESANN 2019
Encoding robust representation for graph generation IJCNN 2019
Labeled graph generative adversarial networks Arxiv 2019
Explore Deep Graph Generation 2019
Graph generation with variational recurrent neural network NeurIPS 2019 Workshop
Learning deep generative models of graphs ICLR 2018 Workshop
Constrained generation of semantically valid graphs via regularizing variational autoencoders NeurIPS 2018
Graph convolutional policy network for goal-directed molecular graph generation NeurIPS 2018
Variational graph auto-encoders NeurIPS 2018 Workshop
Defactor: Differentiable edge factorization-based probabilistic graph generation Arxiv 2018
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models ICML 2018
NetGAN: Generating Graphs via Random Walks ICML 2018
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders ICANN 2018
Syntax-directed variational autoencoder for structured data ICLR 2018
MolGAN: An implicit generative model for small molecular graphs ICML 2018 Workshop
DiPol-GAN: Generating Molecular Graphs Adversarially with Relational Differentiable Pooling 2017
Scene graph generation by iterative message passing CVPR 2017
Generating synthetic decentralized social graphs with local differential privacy SIGSAC 2017
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment NeuroImage 2017
gMark: Schema-Driven Generation of Graphs and Queries TKDM 2016
Functional Dependencies for Graphs ICMD 2016
Composing graphical models with neural networks for structured representations and fast inference NeurlIPS 2016
A synthetic data generator for online social network graphs Social Network Analysis and Mining 2016
A dynamic multiagent genetic algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps IEEE Transactions on Fuzzy Systems 2015
Learning Structured Output Representation using Deep Conditional Generative Models NeurIPS 2015
Graph-based statistical language model for code ICSE 2015
A modularity-based random SAT instances generator IJCAI 2015
How Community-like is the Structure of Synthetically Generated Graphs? Graph Data management Experiences and Systems 2014
Structured generative models of natural source code PMLR 2014
S3G2: A Scalable Structure-Correlated Social Graph Generator TCPEB 2012
Fast random walk graph kernel SDM 2012
An Efficient Generator for Clustered Dynamic Random Networks Mediterranean Conference on Algorithms 2012
Kronecker graphs: An approach to modeling networks JMLR 2010
RTG: a recursive realistic graph generator using random typing KDD 2009
Generation and Analysis of Large Synthetic Social Contact Networks WSC 2009
RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs ICDM 2008
Recent developments in exponential random graph (p*) models for social networks Social networks 2007
Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication European conference on principles of data mining and knowledge discovery 2005
Collective dynamics of ‘smallworld’ networks nature 1998
On the evolution of random graphs Publ. Math. Inst. Hung. Acad. Sci 1960
MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy ICLR 2024
Curvature Filtrations for Graph Generative Model Evaluation NeurIPS 2023
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions ICLR 2022
On Evaluation Metrics for Graph Generative Models ICLR 2022
Metrics for graph comparison: A practitioner’s guide PONE 2022
Efficient 3D Molecular Generation with Flow Matching and Scale Optimal Transport ICML 2024 workshop
Equivariant Flow Matching for Molecular Conformer Generation ICML 2024 workshop
Geometric Latent Diffusion Models for 3D Molecule Generation ICML 2023
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D ICML 2023
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation Arxiv 2023
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation Arxiv 2023
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries NeurIPS 2022
Molecule Generation by Principal Subgraph Mining and Assembling NeurIPS 2022
Exploring Chemical Space with Score-based Out-of-distribution Generation Arxiv 2022
Interpretable molecular graph generation via monotonic constraints SDM 2022
Robust Molecular Image Recognition: A Graph Generation Approach Arxiv 2022
Small molecule generation via disentangled representation learning Bioinformatics 2021
Deep latent-variable models for controllable molecule generation BIBM 2021
Spanning Tree-based Graph Generation for Molecules ICLR 2021
GraphEBM: Molecular graph generation with energy-based models ICLR 2021 Workshop
E(n) Equivariant Normalizing Flows NeurIPS 2021
Nevae: A deep generative model for molecular graphs JMLR 2020
Mol-CycleGAN: a generative model for molecular optimization Journal of Cheminformatics 2020
GraphAF: a flow-based autoregressive model for molecular graph generation ICLR 2020
MoFlow: an invertible flow model for generating molecular graphs KDD 2020
A deep generative model for fragment-based molecule generation AISTATS 2020
A two-step graph convolutional decoder for molecule generation NeurIPS 2019 Workshop
MolecularRNN: generating realistic molecular graphs with optimized properties Arxiv 2019
Graphnvp: An invertible flow model for generating molecular graphs Arxiv 2019
Graph residual flow for molecular graph generation Arxiv 2019
Likelihood-free inference and generation of molecular graphs Arxiv 2019
Scaffold-based molecular design with a graph generative model Chemical Science 2019
Constrained graph variational autoencoders for molecule design NeurIPS 2018
Junction tree variational autoencoder for molecular graph generation ICML 2018
Protein Design with Guided Discrete Diffusion NeurIPS 2023
Generative modeling for protein structures NeurIPS 2018
A generative model for protein contact networks Journal of Biomolecular Structure and Dynamics 2016
Synthetic generators for cloning social network data ASE International Conference on Social Informatics
Deep Generative Graph Distribution Learning for Synthetic Power Grids Arxiv 2019
