Graph Transformers for Query Plan Representation: Potentials and Challenges
Summary: Finds Graph Transformer Networks (GTNs) excel at query-plan representation (new taxonomy) but degrade with scarce training data. Proposes data augmentation and replacing LM-style components in GTNs, yielding SOTA on JOB/TPC-H/TPC-DS and TPC-DS→TPC-H transfer. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chenghao Lyu (University of Massachusetts Amherst)
- 2. Guillaume Lachaud (Ecole Polytechnique)
- 3. Gabriel Lozano (Ecole Polytechnique)
- 4. Yanlei Diao (Ecole Polytechnique; University of Massachusetts Amherst)
BibTeX Citation
@article{lyu_vldb25,
title = {{Graph Transformers for Query Plan Representation: Potentials and Challenges}},
author = {Lyu, Chenghao and Lachaud, Guillaume and Lozano, Gabriel and Diao, Yanlei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {13},
pages = {5716--5730},
doi = {10.14778/3773731.3773745},
url = {https://doi.org/10.14778/3773731.3773745},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,989 | ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB | 2026 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 50 of 52 cited papers.
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