Papers › A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs). Due to the nature of evolving graph structures into the training process, vanilla GNNs usually fail to scale up, limited by the GPU memory space. Up to now, though numerous scalable GNN architectures have been proposed, we still lack a comprehensive survey and fair benchmark of this reservoir to find the rationale for designing scalable GNNs. To this end, we first systematically formulate the representative methods of large-scale graph training into several branches and further establish a fair and consistent benchmark for them by a greedy hyperparameter searching. In addition, regarding efficiency, we theoretically evaluate the time and space complexity of various branches and empirically compare them w.r.t GPU memory usage, throughput, and convergence. Furthermore, We analyze the pros and cons for various branches of scalable GNNs and then present a new ensembling training manner, named EnGCN, to address the existing issues. Our code is available at https://github.com/VITA-Group/Large_Scale_GCN_Benchmarking.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Flickr | EnGCN (Duan et al., 2022) | Accuracy | 0.562 | #4 of 8 | Archive leaderboard | report |
| Node Classification | EnGCN | Accuracy | 96.65% | #7 of 16 | Archive leaderboard | report | |
| Node Property Prediction | ogbn-products | EnGCN | Ext. data | No | #2 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | EnGCN | Number of params | 653918 | #2 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | EnGCN | Test Accuracy | 0.8798 ± 0.0004 | #2 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | EnGCN | Validation Accuracy | 0.9241 ± 0.0003 | #2 of 64 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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