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)
@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}
}
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
Rank
Citing Paper
Year
Venue
Pagerank
PreviousPage 1 / 1Next
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.