Scalable and Efficient Full-Graph GNN Training for Large Graphs
Summary: G3 enables scalable full-graph GNN training on billions of edges with hybrid parallelism across three dimensions and p2p intermediate sharing. Partitioning and pipelining overlap compute and comms, yielding up to 2.24× speedup on 16 nodes. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xinchen Wan (Hong Kong University of Science and Technology)
- 2. Kaiqiang Xu (Hong Kong University of Science and Technology)
- 3. Xudong Liao (Hong Kong University of Science and Technology)
- 4. Yilun Jin (Hong Kong University of Science and Technology)
- 5. Kai Chen (Hong Kong University of Science and Technology)
- 6. Xin Jin (Peking University)
BibTeX Citation
@inproceedings{wan_sigmod23,
title = {{Scalable and Efficient Full-Graph GNN Training for Large Graphs}},
author = {Wan, Xinchen and Xu, Kaiqiang and Liao, Xudong and Jin, Yilun and Chen, Kai and Jin, Xin},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589288},
url = {https://dl.acm.org/doi/10.1145/3589288},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 203 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00025234479 |
| 1,021 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB | 0.00012408574 |
| 1,047 | SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks | 2022 | VLDB | 0.00012313386 |
| 2,273 | NeutronStar: Distributed GNN Training with Hybrid Dependency Management | 2022 | SIGMOD | 8.7079974e-05 |
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