ByteGNN: Efficient Graph Neural Network Training at Large Scale
Summary: ByteGNN enables scalable distributed GNN training with three designs: mini-batch sampling for high parallelism; a two-level scheduler for better resource use; and a GNN-aware partitioner. 3.5–23.8x end-to-end speedups, 2–6x CPU, ~50% network-cost reduction. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chenguang Zheng (ByteDance; Chinese University of Hong Kong)
- 2. Hongzhi Chen (ByteDance)
- 3. Yuxuan Cheng (ByteDance)
- 4. Zhezheng Song (Chinese University of Hong Kong)
- 5. Yifan Wu (ByteDance; Peking University)
- 6. Changji Li (ByteDance; Chinese University of Hong Kong)
- 7. James Cheng (Chinese University of Hong Kong)
- 8. Hao Yang (ByteDance)
- 9. Shuai Zhang (ByteDance)
BibTeX Citation
@article{zheng_vldb22,
title = {{ByteGNN: Efficient Graph Neural Network Training at Large Scale}},
author = {Zheng, Chenguang and Chen, Hongzhi and Cheng, Yuxuan and Song, Zhezheng and Wu, Yifan and Li, Changji and Cheng, James and Yang, Hao and Zhang, Shuai},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {6},
pages = {1228--1242},
doi = {10.14778/3514061.3514069},
url = {https://doi.org/10.14778/3514061.3514069},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 21 of 21 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 223 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00024182473 |
| 937 | Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs | 2014 | VLDB | 0.00013091546 |
| 1,048 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB | 0.00012433693 |
| 6,375 | MIFO: A Query-Semantic Aware Resource Allocation Policy | 2019 | SIGMOD | 5.8932222e-05 |
| 11,662 | Vertex-Centric Visual Programming for Graph Neural Networks | 2021 | SIGMOD | 5.093636e-05 |
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