Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching
Summary: Ginex enables billion-scale GNN training on a single machine with SSDs. Inspector-executor pipeline splits sample/gather, enabling Belady-like optimal in-memory caching for feature vectors. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yeonhong Park (Seoul National University)
- 2. Sunhong Min (Seoul National University)
- 3. Jae W. Lee (Seoul National University)
BibTeX Citation
@article{park_vldb22,
title = {{Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching}},
author = {Park, Yeonhong and Min, Sunhong and Lee, Jae W.},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2626--2639},
doi = {10.14778/3551793.3551819},
url = {https://doi.org/10.14778/3551793.3551819},
year = {2022}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 223 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00024182473 |
| 1,048 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB | 0.00012433693 |
| 1,234 | Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture | 2021 | VLDB | 0.00011549432 |
| 3,740 | GTS: A Fast and Scalable Graph Processing Method based on Streaming Topology to GPUs | 2016 | SIGMOD | 7.1593902e-05 |
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