ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling
Summary: ADGNN enables scalable full-batch GNN training with a hybrid sampling engine in distributed systems. It introduces Aggregation Difference (AD) to bound sampling impact, plus AD-Sampling with adaptive sampling and AD-importance sampling for remote nodes, with result reuse; achieving up to 9x efficiency and similar accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhen Song (Northeastern University)
- 2. Yu Gu (Northeastern University)
- 3. Tianyi Li (Aalborg University)
- 4. Qing Sun (Northeastern University)
- 5. Yanfeng Zhang (Northeastern University)
- 6. Christian S. Jensen (Aalborg University)
- 7. Ge Yu (Northeastern University)
BibTeX Citation
@inproceedings{song_sigmod23,
title = {{ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling}},
author = {Song, Zhen and Gu, Yu and Li, Tianyi and Sun, Qing and Zhang, Yanfeng and Jensen, Christian S. and Yu, Ge},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626716},
url = {https://dl.acm.org/doi/10.1145/3626716},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,760 | DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training | 2024 | VLDB | 6.0961929e-05 |
| 10,241 | FastGNAS: Accelerating and Scaling Graph Neural Architecture Search on Multi-GPUs via Ring-Based Model Migration | 2026 | SIGMOD | 5.093636e-05 |
| 10,309 | A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness | 2026 | SIGMOD | 5.093636e-05 |
| 10,780 | SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training | 2025 | SIGMOD | 5.093636e-05 |
| 10,811 | Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch | 2025 | VLDB | 5.093636e-05 |
| 10,975 | Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 223 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00024182473 |
| 1,048 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB | 0.00012433693 |
| 2,640 | Scalable and Efficient Full-Graph GNN Training for Large Graphs | 2023 | SIGMOD | 8.3074486e-05 |
| 2,668 | DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU | 2023 | SIGMOD | 8.2750247e-05 |
| 2,695 | NeutronStar: Distributed GNN Training with Hybrid Dependency Management | 2022 | SIGMOD | 8.2468134e-05 |
| 3,634 | Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees | 2023 | SIGMOD | 7.2358691e-05 |
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