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
- 2. Yuntao Gui
- 3. Tatiana Jin
- 4. James Cheng
- 5. Xiao Yan
- 6. Peiqi Yin
- 7. Yufei Cai
- 8. Bo Tang
- 9. Fan Yu
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,399 | ByteGNN: Efficient Graph Neural Network Training at Large Scale | 2022 | VLDB | 8.8869693e-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 |
|---|---|---|---|---|
| 4 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0019040811 |
| 1,170 | Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs | 2014 | VLDB | 0.00013526297 |
| 3,239 | Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps | 2016 | VLDB | 7.3284186e-05 |
| 5,625 | High Performance Distributed OLAP on Property Graphs with Grasper | 2020 | SIGMOD | 5.4048567e-05 |
| 6,706 | Big Graph Analytics Systems | 2016 | SIGMOD | 4.9501419e-05 |
| 9,862 | Large Scale Graph Mining with G-Miner | 2019 | SIGMOD | 4.2641627e-05 |
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