In-Memory Subgraph Matching: An In-depth Study
Summary: Eight in-memory subgraph-matchers are compared in a common framework across filtering, ordering, enumeration, and optimizations. Findings: GraphQL filtering is competitive; RI ordering is strong; DP-iso pruning helps large queries; code available. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shixuan Sun (Hong Kong University of Science and Technology)
- 2. Qiong Luo (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{sun_sigmod20,
title = {{In-Memory Subgraph Matching: An In-depth Study}},
author = {Sun, Shixuan and Luo, Qiong},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380581},
url = {https://dl.acm.org/doi/10.1145/3318464.3380581},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 53 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,072 | Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds | 2025 | VLDB | 5.093636e-05 |
| 11,075 | Mix & Match: Subgraph Matching for Absolute Coverage | 2025 | VLDB | 5.093636e-05 |
| 11,165 | gSWORD: GPU-accelerated Sampling for Subgraph Counting | 2024 | SIGMOD | 5.093636e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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