Efficient GPU-Accelerated Local Subgraph Counting

Summary: GPU-accelerated local subgraph counting retains SCOPE’s tree decomposition while addressing GPU memory limits via compressed intermediates and correctness-preserving insert-failure restarts. Lock-free hash tables and key mapping deliver up to 35× speedup, enabling million-scale graphs. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID: h2773a1d3c94c650c
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,882 | 26.84%
DOI: 10.14778/3836663.3836711

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Qiao He (Hong Kong Polytechnic University)
2. Yiran Li (University of Toronto)
3. Man Lung Yiu (Hong Kong Polytechnic University)
4. Jieming Shi (Hong Kong Polytechnic University)

BibTeX Citation

@article{he_vldb26,
        title = {{Efficient GPU-Accelerated Local Subgraph Counting}},
        author = {He, Qiao and Li, Yiran and Yiu, Man Lung and Shi, Jieming},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {11},
        pages = {3592--3605},
        doi = {10.14778/3836663.3836711},
        url = {https://doi.org/10.14778/3836663.3836711},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
712	Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins	2019	VLDB	0.00014578373
1,180	In-Memory Subgraph Matching: An In-depth Study	2020	SIGMOD	0.00011627669
2,342	Learned Cardinality Estimation: An In-depth Study	2022	SIGMOD	8.6060437e-05
2,468	GPU-Accelerated Subgraph Enumeration on Partitioned Graphs	2020	SIGMOD	8.4178183e-05
2,522	Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation	2022	VLDB	8.3477168e-05
2,634	Neural Subgraph Counting with Wasserstein Estimator	2022	SIGMOD	8.1993804e-05
2,824	G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching	2020	SIGMOD	7.9698957e-05
3,011	Efficient GPU-Accelerated Subgraph Matching	2023	SIGMOD	7.7549462e-05
3,160	A Learned Sketch for Subgraph Counting	2021	SIGMOD	7.5807496e-05
5,422	Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach	2024	VLDB	6.1361195e-05
5,901	Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation	2021	SIGMOD	5.9539374e-05
7,529	Fast Local Subgraph Counting	2024	VLDB	5.5009945e-05
8,796	GPH: An Efficient and Effective Perfect Hashing Scheme for GPU Architectures	2025	SIGMOD	5.2748571e-05
9,650	gSWORD: GPU-accelerated Sampling for Subgraph Counting	2024	SIGMOD	5.1453267e-05

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