Repo link: AI4Risk/GraphGenerator
AI4Risk/GraphGenerator integrates various pre-processing codes for static and dynamic graph data, multiple learning-based methods for static and dynamic graph generation, as well as related evaluation tools.
BTGAE: Divide and Conquer: A Topological Heterogeneity-based Framework for Scalable and Realistic Graph Generation. (Official PyTorch Implementation)CPGAE: Efficient Learning-based Community-Preserving Graph Generation, in ICDE 2022. (GAE version of CPGAN)VRDAG: Efficient Dynamic Attributed Graph Generation, in ICDE 2025.TGAE: Efficient Learning-based Graph Simulation for Temporal Graphs, in ICDE 2025.
Implementation of baselines:
GraphRNN: GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, in ICML 2018.
The following datasets are mainly from linqs and snap.
| Data | #Nodes | #Edges | GINI | PWE | |
|---|---|---|---|---|---|
| citeseer | 3,327 | 4,732 | 2.774 | 0.435 | 2.420 |
| cora | 2,708 | 5,429 | 3.898 | 0.405 | 1.932 |
| pubmed | 19,717 | 44,338 | 4.496 | 0.604 | 2.176 |
| Epinions | 75,879 | 508,837 | 10.694 | 0.805 | 2.026 |
| 875,713 | 5,105,039 | 9.871 | 0.587 | 1.617 | |
| YelpChi | 45,954 | 3,846,979 | 167.427 | 0.322 | 1.205 |
GINI: GINI index, which is a common measure for inequality in a degree distribution.
PWE: power-law exponent.
The following temporal network datasets are from linqs and Network Repository. For more information, please refer to the VRDAG paper.
| Data | #Nodes | #Edges | T |
|---|---|---|---|
| Emails-DNC | 1,891 | 39,264 | 14 |
| Bitcoin-Alpha | 3,783 | 24,186 | 37 |
| Wiki-Vote | 7,115 | 103,689 | 43 |
If you find GraphGenerator is useful for your research, please consider citing the following papers:
@inproceedings{xiang2022efficient,
title={Efficient learning-based community-preserving graph generation},
author={Xiang, Sheng and Cheng, Dawei and Zhang, Jianfu and Ma, Zhenwei and Wang, Xiaoyang and Zhang, Ying},
booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
pages={1982--1994},
year={2022},
organization={IEEE}
}
@inproceedings{li2025efficient,
title={Efficient Dynamic Attributed Graph Generation},
author={Li, Fan and Wang, Xiaoyang and Cheng, Dawei and Chen, Cong and Zhang, Ying and Lin, Xuemin},
booktitle={2025 IEEE 41th International Conference on Data Engineering (ICDE)},
year={2025},
organization={IEEE}
}
@inproceedings{xiang2025efficient,
title={Efficient Learning-based Graph Simulation for Temporal Graphs},
author={Xiang, Sheng and Xu, Chenhao and Cheng, Dawei and Wang, Xiaoyang and Zhang, Ying},
booktitle={2025 IEEE 41th International Conference on Data Engineering (ICDE)},
year={2025},
organization={IEEE}
}