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Scalable and Efficient Full-Graph GNN Training for Large Graphs
Summary: G3 enables scalable full-graph GNN training on billions of edges with hybrid parallelism across three dimensions and p2p intermediate sharing. Partitioning and pipelining overlap compute and comms, yielding up to 2.24× speedup on 16 nodes.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6647
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 7.5869574e-05
- Overall Rank
- 3,092 | 78.52%
- DOI
-
10.1145/3589288
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,721 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3538607e-05 |
| 5,746 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3429324e-05 |
| 7,286 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7701372e-05 |
| 7,568 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7044808e-05 |
| 9,400 |
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism |
2025 |
VLDB |
4.3399748e-05 |
| 9,785 |
Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs |
2025 |
SIGMOD |
4.2799988e-05 |
| 9,787 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2799988e-05 |
| 10,027 |
NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,310 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
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 |
| 10,579 |
NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism |
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 |
| 11,029 |
Improving Graph Compression for Efficient Resource-Constrained Graph Analytics |
2024 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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ByteGNN: Efficient Graph Neural Network Training at Large Scale |
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VLDB |
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