Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation
Summary: COLOR applies graph-coloring insights from graph compression to build compact, topology-aware summaries for subgraph cardinality estimation. Optimized inference handles large many-to-many graph queries, delivering up to 10³× better accuracy with fast construction, low memory, and update robustness. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kyle Deeds (University of Washington)
- 2. Diandre Sabale (University of Washington)
- 3. Moe Kayali (University of Washington)
- 4. Dan Suciu (University of Washington)
BibTeX Citation
@article{deeds_vldb25,
title = {{Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation}},
author = {Deeds, Kyle and Sabale, Diandre and Kayali, Moe and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {2},
pages = {130--143},
doi = {10.14778/3705829.3705834},
url = {https://doi.org/10.14778/3705829.3705834},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,184 | Path-centric Cardinality Estimation for Subgraph Matching | 2025 | VLDB | 5.0651993e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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