Efficient and Accurate Subgraph Counting: A Bottom-up Flow-learning Based Approach
Summary: FlowSC combines stronger bipartite candidate filtering with a bottom-up flow-learning GNN that simulates candidate-tree counting via controlled message flow and aggregation. It delivers up to 4-order-of-magnitude higher accuracy and 3× speedups, scaling to billion-edge graphs. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Qiuyu Guo (Guangdong University of Technology; University of New South Wales)
- 2. Jianye Yang (Guangdong University of Technology; Peng Cheng Laboratory)
- 3. Wenjie Zhang (University of New South Wales)
- 4. Hanchen Wang (University of New South Wales)
- 5. Ying Zhang (Zhejiang Gongshang University)
- 6. Xuemin Lin (Shanghai Jiao Tong University)
BibTeX Citation
@article{guo_vldb25,
title = {{Efficient and Accurate Subgraph Counting: A Bottom-up Flow-learning Based Approach}},
author = {Guo, Qiuyu and Yang, Jianye and Zhang, Wenjie and Wang, Hanchen and Zhang, Ying and Lin, Xuemin},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {8},
pages = {2695--2708},
doi = {10.14778/3742728.3742758},
url = {https://doi.org/10.14778/3742728.3742758},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,520 | Efficient Temporal Subgraph Management: A New Interval Index | 2026 | VLDB | 5.093636e-05 |
| 11,067 | Machine Learning for Graph Data Management and Query Processing | 2025 | VLDB | 5.093636e-05 |
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