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Efficient Training of Graph Neural Networks on Large Graphs
Summary: Tutorial framing efficient GNN training on massive graphs via a data-management perspective across the graph lifecycle—preprocessing, batching, data transfer, and model training. Surveys DB techniques for static vs. dynamic graphs and pinpoints open research directions.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13625
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 4.8922884e-05
- Overall Rank
- 6,942 | 51.71%
- DOI
-
10.14778/3685800.3685844
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
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 |
| 1,138 |
Traversing Large Graphs on GPUs with Unified Memory |
2020 |
VLDB |
0.00013727765 |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 2,422 |
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU |
2023 |
SIGMOD |
8.8499665e-05 |
| 3,025 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6906935e-05 |
| 3,709 |
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank |
2023 |
VLDB |
6.8242482e-05 |
| 4,047 |
Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees |
2023 |
SIGMOD |
6.4972105e-05 |
| 5,136 |
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments |
2024 |
VLDB |
5.6723526e-05 |
| 5,475 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4869706e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8616315e-05 |
| 7,289 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7747168e-05 |
| 13,150 |
STile: Searching Hybrid Sparse Formats for Sparse Deep Learning Operators Automatically |
2024 |
SIGMOD |
- |
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| Overall Rank |
Paper |
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Venue |
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| 5,443 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.5025808e-05 |
| 10,647 |
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study |
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4.1945683e-05 |
| 10,735 |
Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation |
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4.1945683e-05 |
| 3,986 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
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6.5611714e-05 |
| 5,561 |
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses |
2024 |
VLDB |
5.4332062e-05 |
| 10,233 |
Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling |
2026 |
VLDB |
4.1945683e-05 |
| 10,539 |
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch. |
2025 |
VLDB |
4.1945683e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |