A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction
Summary: Comprehensive survey and experimental study of subgraph matching, examining current trends, unbiased evaluation, and cross-stage interactions under the embedding enumeration framework. Shows backtracking enhancements as a dominant trend, demonstrates powerful bias from output-size limits, and uses embeddings per second to compare 10 techniques per stage across all feasible combinations, and fosters reproducible comparisons across benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Zhijie Zhang (Fudan University)
- 2. Yujie Lu (Fudan University)
- 3. Weiguo Zheng (Fudan University)
- 4. Xuemin Lin (Shanghai Jiao Tong University)
BibTeX Citation
@inproceedings{zhang_sigmod24,
title = {{A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction}},
author = {Zhang, Zhijie and Lu, Yujie and Zheng, Weiguo and Lin, Xuemin},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639315},
url = {https://dl.acm.org/doi/10.1145/3639315},
year = {2024}
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