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Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch

Summary: Cross-category empirical study of full-graph vs. mini-batch GNN training systems, advocating time-to-accuracy—not epoch time—as the fair metric. Across datasets, models, and configurations, mini-batch converges faster and achieves similar or higher accuracy, refuting sampling concerns. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13978
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,811 | 25.83%
DOI
10.14778/3717755.3717776

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BibTeX Citation

@article{bajaj_vldb25,
        title = {{Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch}},
        author = {Bajaj, Saurabh and Son, Hojae and Liu, Juelin and Guan, Hui and Serafini, Marco},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {4},
        pages = {1196--1209},
        doi = {10.14778/3717755.3717776},
        url = {https://doi.org/10.14778/3717755.3717776},
        year = {2025}
}

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