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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)

Paper ID
13987
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,817 | 25.79%
DOI
10.14778/3718057.3718061

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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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