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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
- 13626
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 4.8875946e-05
- Overall Rank
- 6,944 | 51.74%
- 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 |
0.00013715114 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 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 |
| 3,715 |
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank |
2023 |
VLDB |
6.8176818e-05 |
| 4,054 |
Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees |
2023 |
SIGMOD |
6.4910804e-05 |
| 5,135 |
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments |
2024 |
VLDB |
5.6669017e-05 |
| 5,485 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4817019e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8570865e-05 |
| 7,286 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7701372e-05 |
| 13,164 |
STile: Searching Hybrid Sparse Formats for Sparse Deep Learning Operators Automatically |
2024 |
SIGMOD |
- |
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 5,452 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.4972958e-05 |
| 10,655 |
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study |
2025 |
VLDB |
4.1905499e-05 |
| 10,742 |
Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation |
2025 |
VLDB |
4.1905499e-05 |
| 3,988 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5548465e-05 |
| 5,570 |
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses |
2024 |
VLDB |
5.4280174e-05 |
| 10,233 |
Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling |
2026 |
VLDB |
4.1905499e-05 |
| 10,548 |
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch. |
2025 |
VLDB |
4.1905499e-05 |
| 2,399 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8869693e-05 |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |
| 5,746 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3429324e-05 |