OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
Summary: OpenFGL standardizes FGL evaluation across Graph-FL and Subgraph-FL with 42 datasets, 8 partitioning strategies, 5 tasks, and 18 SOTA algorithms. It uniquely exposes effects of graph heterogeneity, robustness, and efficiency via reproducible cross-domain benchmarking. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Xunkai Li (Beijing Institute of Technology)
- 2. Yinlin Zhu (Sun Yat-Sen University)
- 3. Boyang Pang (Beijing Institute of Technology)
- 4. Guochen Yan (Peking University)
- 5. Yeyu Yan (Beijing Jiaotong University)
- 6. Zening Li (Beijing Institute of Technology)
- 7. Zhengyu Wu (Beijing Institute of Technology)
- 8. Wentao Zhang (Peking University)
- 9. Rong-Hua Li (Beijing Institute of Technology)
- 10. Guoren Wang (Beijing Institute of Technology)
BibTeX Citation
@article{li_vldb25,
title = {{OpenFGL: A Comprehensive Benchmark for Federated Graph Learning}},
author = {Li, Xunkai and Zhu, Yinlin and Pang, Boyang and Yan, Guochen and Yan, Yeyu and Li, Zening and Wu, Zhengyu and Zhang, Wentao and Li, Rong-Hua and Wang, Guoren},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {5},
pages = {1305--1320},
doi = {10.14778/3718057.3718061},
url = {https://doi.org/10.14778/3718057.3718061},
year = {2025}
}
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