- 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]