Mix & Match: Subgraph Matching for Absolute Coverage
Summary: Targets coverage in subgraph matching: existing enumerators produce localized, biased streams when matches are massive, so the goal is to quickly surface results representative of the whole graph. M&M mixes global exploration (prioritizing first-level backtracking to expand coverage) with local pruning of non-coverage paths, finding ~2× more unique nodes than prior work. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Konstantinos Skitsas (Aarhus University)
- 2. Yuya Sasaki (University of Osaka)
- 3. Davide Mottin (Aarhus University)
- 4. Panagiotis Karras (Aarhus University; University of Copenhagen)
BibTeX Citation
@article{skitsas_vldb25,
title = {{Mix \& Match: Subgraph Matching for Absolute Coverage}},
author = {Skitsas, Konstantinos and Sasaki, Yuya and Mottin, Davide and Karras, Panagiotis},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {13},
pages = {5610--5622},
doi = {10.14778/3773731.3773737},
url = {https://doi.org/10.14778/3773731.3773737},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,506 | Sublime: Selecting Subgraph Matching Algorithms via Machine Learning | 2026 | SIGMOD | 4.9793485e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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