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Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch.
Summary: Comprehensive empirical comparison of full-graph and mini-batch GNN training systems across datasets, models, and configurations. Mini-batch consistently converges faster and attains similar or better accuracy; urges time-to-accuracy metrics and per-method hyperparameter tuning.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13791
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,548 | 26.70%
- DOI
-
10.14778/3717755.3717776
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
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.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 271 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029565193 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 1,332 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
2020 |
VLDB |
0.00012549751 |
| 2,399 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8869693e-05 |
| 2,425 |
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU |
2023 |
SIGMOD |
8.8414587e-05 |
| 3,028 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6833093e-05 |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |
| 5,135 |
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments |
2024 |
VLDB |
5.6669017e-05 |
| 5,746 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3429324e-05 |
| 7,087 |
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs |
2023 |
SIGMOD |
4.8324242e-05 |
| 7,568 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7044808e-05 |
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