• Update: This repository is actively updated. 2024/5/21
• Collection: We've compiled a comprehensive list of synthetic graph generators.
• Collaborate: If there’s anything missing or if you'd like to contribute, please don't hesitate to get in touch!

• Learning based generative models
• Graph Autoencoders
• Traditional generative models
• Configuration models
• Evaluation
• Books

Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling Xiaohui Chen, Jiaxing He Xu Han, Li-Ping Liu ICML 2023. [paper]

SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu, Carsten Maple, Gesine Reinert, Andrew Elliott Arxiv 2023. [paper]

FlowGEN: A Generative Model for Flow Graphs Furkan Kocayusufoglu, A. Silva, Ambuj K. Singh KDD 2022. [paper]

Efficient Learning-based Community-Preserving Graph Generation Sheng Xiang, Dawei Cheng, Jianfu Zhang, Zhenwei Ma, Xiaoyang Wang, Ying Zhang ICDE 2022. [paper]

Scalable Deep Generative Modeling for Sparse Graphs Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans ICML 2020. [paper]

NetGAN without GAN: From Random Walks to Low-Rank Approximations Luca Rendsburg, Holger Heidrich, Ulrike Von Luxburg ICML 2020. [paper]

Variational graph recurrent neural networks Ehsan Hajiramezanali∗, Arman Hasanzadeh∗, Nick Duffield, Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian NIPS 2019. [paper]

Stochastic Blockmodels meet Graph Neural Networks Nikhil Mehta, Lawrence Carin Duke, Piyush Rai ICML 2019. [paper]

Graphite: Iterative Generative Modeling of Graphs Aditya Grover, Aaron Zweig, Stefano Ermon ICML 2019. [paper]

Efficient Graph Generation with Graph Recurrent Attention Networks Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel NeurIPS 2019. [paper]

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec ICML 2018. [paper]

NetGAN: Generating Graphs via Random Walks Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann ICML 2018. [paper]

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner

Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang

WWW 2023. [paper]

GraphMAE: Self-Supervised Masked Graph Autoencoders

Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang

KDD 2022. [paper]

Adaptive Graph Encoder for Attributed Graph Embedding

Ganqu Cui, Jie Zhou, Cheng Yang, Zhiyuan Liu

KDD 2020. [paper]

GPT-GNN: Generative Pre-Training of Graph Neural Networks

Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun

KDD 2020. [paper]

Adversarially Regularized Graph Autoencoder for Graph Embedding

Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang

IJCAI 2019. [paper]

Adversarially Regularized Graph Autoencoder for Graph Embedding

Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang

IJCAI 2019. [paper]

MGAE: Marginalized Graph Autoencoder for Graph Clustering

Chun Wang, PictureShirui Pan, PictureGuodong Long, PictureXingquan Zhu, PictureJing Jiang

CIKM 2017. [paper]

Variational Graph Auto-Encoders

Thomas N. Kipf, Max Welling

NIPS 2016. [paper]

A Scalable Generative Graph Model with Community Structure

Tamara G. Kolda, Ali Pinar, Todd Plantenga, C. Seshadhri

SIAM 2014. [paper]

Kronecker Graphs: An Approach to Modeling Networks

Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani

JMLR 2010. [paper]

R-MAT: A Recursive Model for Graph Mining

Deepayan Chakrabarti, Yiping Zhan, Christos Faloutsos SIAM 2004. [paper]

Community Detection in Networks with Node Attributes

Jaewon Yang, Julian McAuley, Jure Leskovec

ICDM 2013. [paper]

Community detection in graphs(Stochastic BlockModels) Santo Fortunato

Physics Reports 2010 [paper]

Erdős–Rényi model

P. Erdős, A. Rényi (Budapest).

Publicationes Mathematicae 1959. [paper]

On the Power of Edge Independent Graph Models

Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis

NIPS 21. [paper]

On the Role of Edge Dependency in Graph Generative Models

Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis

Arxiv 2023. [paper]

Inductive Bias in Machine Learning

Luca Silvester Rendsburg

Arxiv 2023. [book]