SUFF: Accelerating Subgraph Matching with Historical Data
Summary: SUFF is a general structure-filtering framework that derives compact, sound filters from past subgraph-matching results to prune future search spaces and augment existing matching algorithms with minimal changes. Practical optimizations cut build/store costs and yield up to 15× speedups on real graphs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xun Jian (Hong Kong University of Science and Technology)
- 2. Zhiyuan Li (Hong Kong University of Science and Technology)
- 3. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@article{jian_vldb23,
title = {{SUFF: Accelerating Subgraph Matching with Historical Data}},
author = {Jian, Xun and Li, Zhiyuan and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {7},
pages = {1699--1711},
doi = {10.14778/3587136.3587144},
url = {https://doi.org/10.14778/3587136.3587144},
year = {2023}
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