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Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
Summary: Empirically evaluates 12 graph reordering strategies across PyTorch Geometric and DGL, showing reordering can reduce GNN training time on both CPU and GPU. Finds effectiveness depends on GNN hyperparameters and reordering metrics, lightweight reorders favor GPUs, and reordering costs often amortize.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13932
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,647 | 25.94%
- DOI
-
10.14778/3705829.3705846
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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 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 278 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029230623 |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 1,676 |
Speedup Graph Processing by Graph Ordering |
2016 |
SIGMOD |
0.00010946423 |
| 1,968 |
An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing |
2017 |
VLDB |
9.9071968e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 2,494 |
Streaming Graph Partitioning: An Experimental Study |
2018 |
VLDB |
8.6508229e-05 |
| 3,839 |
Experimental Analysis of Streaming Algorithms for Graph Partitioning |
2019 |
SIGMOD |
6.7120651e-05 |
| 3,986 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5611714e-05 |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |
| 5,949 |
Hybrid Edge Partitioner: Partitioning Large Power-Law Graphs under Memory Constraints |
2021 |
SIGMOD |
5.2595857e-05 |
| 7,108 |
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training |
2025 |
SIGMOD |
4.8297805e-05 |
| 8,254 |
A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms |
2019 |
VLDB |
4.5491792e-05 |
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