Sublime: Selecting Subgraph Matching Algorithms via Machine Learning
Summary: Sublime replaces brittle hand-crafted algorithm-selection rules for subgraph matching with learned selection from query/data features and labeled performance observations. It improves embeddings/sec by up to 36% and coverage by 46.3% over baselines. (summarized by gpt-5.6-luna on Jul 26 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Genryu Kuraya (The University of Osaka)
- 2. Konstantinos Skitsas (Aarhus University)
- 3. Davide Mottin (Aarhus University)
- 4. Panagiotis Karras (University of Copenhagen)
- 5. Daichi Amagata (The University of Osaka)
- 6. Yuya Sasaki (The University of Osaka)
BibTeX Citation
@inproceedings{kuraya_sigmod26,
title = {{Sublime: Selecting Subgraph Matching Algorithms via Machine Learning}},
author = {Kuraya, Genryu and Skitsas, Konstantinos and Mottin, Davide and Karras, Panagiotis and Amagata, Daichi and Sasaki, Yuya},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802115},
url = {https://dl.acm.org/doi/10.1145/3802115},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
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.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,075 | Mix & Match: Subgraph Matching for Absolute Coverage | 2025 | VLDB |
| 2 | 659 | Efficient Subgraph Matching by Postponing Cartesian Products | 2016 | SIGMOD |
| 3 | 5,118 | Efficient Streaming Subgraph Isomorphism with Graph Neural Networks | 2021 | VLDB |
| 4 | 10,564 | gMatch: Fine-Grained and Hardware-Efficient Subgraph Matching on GPUs | 2026 | VLDB |
| 5 | 442 | Efficient Subgraph Matching on Billion Node Graphs | 2012 | VLDB |
| 6 | 2,190 | Versatile Equivalences: Speeding up Subgraph Query Processing and Subgraph Matching | 2021 | SIGMOD |
| 7 | 4,549 | Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning | 2022 | VLDB |
| 8 | 6,149 | Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding | 2024 | VLDB |
| 9 | 8,256 | Machine Learning for Subgraph Extraction: Methods, Applications and Challenges | 2023 | VLDB |
| 10 | 4,983 | A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction | 2024 | SIGMOD |