Vertex-Centric Visual Programming for Graph Neural Networks
Summary: Seastar introduces a vertex-centric GNN training framework with automatic kernel generation, reducing memory and data movement versus tensor-centric systems. A visual drag-and-drop interface or vertex-centric Python API, with operator fusion and constant folding, speeds convergence and throughput. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Yidi Wu (Chinese University of Hong Kong)
- 2. Yuntao Gui (Chinese University of Hong Kong)
- 3. Tatiana Jin (Chinese University of Hong Kong)
- 4. James Cheng (Chinese University of Hong Kong)
- 5. Xiao Yan (Southern University of Science and Technology)
- 6. Peiqi Yin (Southern University of Science and Technology)
- 7. Yufei Cai (Southern University of Science and Technology)
- 8. Bo Tang (Southern University of Science and Technology)
- 9. Fan Yu (Huawei)
BibTeX Citation
@inproceedings{wu_sigmod21,
title = {{Vertex-Centric Visual Programming for Graph Neural Networks}},
author = {Wu, Yidi and Gui, Yuntao and Jin, Tatiana and Cheng, James and Yan, Xiao and Yin, Peiqi and Cai, Yufei and Tang, Bo and Yu, Fan},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452770},
url = {https://dl.acm.org/doi/10.1145/3448016.3452770},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,863 | ByteGNN: Efficient Graph Neural Network Training at Large Scale | 2022 | VLDB | 9.5950349e-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 |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 937 | Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs | 2014 | VLDB | 0.00013091546 |
| 3,085 | Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps | 2016 | VLDB | 7.7709242e-05 |
| 5,328 | High Performance Distributed OLAP on Property Graphs with Grasper | 2020 | SIGMOD | 6.2636583e-05 |
| 6,450 | Big Graph Analytics Systems | 2016 | SIGMOD | 5.8753826e-05 |
| 10,010 | Large Scale Graph Mining with G-Miner | 2019 | SIGMOD | 5.1776969e-05 |
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