Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds
Summary: Minting accelerates single-graph top-k subgraph mining under MNI by pruning candidates with tight upper bounds and reducing MNI computation via lower/upper bounds. It achieves up to 10^3× speedups and remains practical for large k. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Seonho Lee (Seoul National University)
- 2. Yeunjun Lee (Seoul National University)
- 3. Kunsoo Park (Seoul National University)
BibTeX Citation
@article{lee_vldb25,
title = {{Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds}},
author = {Lee, Seonho and Lee, Yeunjun and Park, Kunsoo},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {3},
pages = {557--570},
doi = {10.14778/3712221.3712225},
url = {https://doi.org/10.14778/3712221.3712225},
year = {2025}
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