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Efficient and Accurate Subgraph Counting: A Bottom-up Flow-learning Based Approach
Summary: FlowSC melds an enhanced bipartite candidate-filtering step with a novel bottom-up flow-learning GNN that explicitly controls message-passing direction, range and iterations to simulate candidate-tree subgraph counting. With customized aggregation and pretraining it achieves up to 10^4× accuracy gains and ≈3× speedups while scaling to billion-edge graphs.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13912
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,632 | 26.04%
- DOI
-
10.14778/3742728.3742758
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No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 28 of 28 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 461 |
Graphs-at-a-time: Query Language and Access Methods for Graph Databases |
2008 |
SIGMOD |
0.00022499343 |
| 612 |
Taming Verification Hardness: An Efficient Algorithm for Testing Subgraph Isomorphism |
2008 |
VLDB |
0.0001920234 |
| 613 |
Design and Implementation of the LogicBlox System |
2015 |
SIGMOD |
0.00019181325 |
| 764 |
TurboISO: Towards UltraFast and Robust Subgraph Isomorphism Search in Large Graph Databases |
2013 |
SIGMOD |
0.00017018712 |
| 943 |
Wander Join: Online Aggregation via Random Walks |
2016 |
SIGMOD |
0.00015145883 |
| 964 |
G-CORE: A Core for Future Graph Query Languages |
2018 |
SIGMOD |
0.0001497475 |
| 1,180 |
Efficient Subgraph Matching by Postponing Cartesian Products |
2016 |
SIGMOD |
0.00013456907 |
| 1,369 |
Random Sampling over Joins Revisited |
2018 |
SIGMOD |
0.00012339777 |
| 1,561 |
Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together |
2019 |
SIGMOD |
0.00011358946 |
| 1,775 |
CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching |
2019 |
SIGMOD |
0.00010602927 |
| 1,924 |
In-Memory Subgraph Matching: An In-depth Study |
2020 |
SIGMOD |
0.00010077055 |
| 2,142 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
9.4507296e-05 |
| 2,162 |
Scalable Subgraph Enumeration in MapReduce |
2015 |
VLDB |
9.3964337e-05 |
| 2,801 |
Scalable Distributed Subgraph Enumeration |
2017 |
VLDB |
8.0992955e-05 |
| 3,001 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
7.7404487e-05 |
| 3,036 |
RapidMatch: A Holistic Approach to Subgraph Query Processing |
2021 |
VLDB |
7.6735171e-05 |
| 3,187 |
Versatile Equivalences: Speeding up Subgraph Query Processing and Subgraph Matching |
2021 |
SIGMOD |
7.4136521e-05 |
| 3,410 |
Motivo: fast motif counting via succinct color coding and adaptive sampling |
2019 |
VLDB |
7.1253867e-05 |
| 3,778 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
6.7747398e-05 |
| 4,470 |
GuP: Fast Subgraph Matching by Guard-based Pruning |
2023 |
SIGMOD |
6.1557462e-05 |
| 5,009 |
HUGE: An Efficient and Scalable Subgraph Enumeration System |
2021 |
SIGMOD |
5.761237e-05 |
| 6,259 |
Neural Attributed Community Search at Billion Scale |
2023 |
SIGMOD |
5.1355079e-05 |
| 6,281 |
A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction |
2024 |
SIGMOD |
5.128862e-05 |
| 6,289 |
Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach |
2024 |
VLDB |
5.1275309e-05 |
| 6,441 |
Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding |
2024 |
VLDB |
5.0603113e-05 |
| 6,575 |
Fast Continuous Subgraph Matching over Streaming Graphs via Backtracking Reduction |
2023 |
SIGMOD |
5.0052259e-05 |
| 6,704 |
Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation |
2021 |
SIGMOD |
4.9554912e-05 |
| 9,730 |
TC-Match: Fast Time-constrained Continuous Subgraph Matching |
2024 |
VLDB |
4.2942813e-05 |
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| 2,903 |
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| 3,778 |
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SIGMOD |
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| 3,001 |
Neural Subgraph Counting with Wasserstein Estimator |
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SIGMOD |
7.7404487e-05 |
| 7,934 |
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2024 |
VLDB |
4.613363e-05 |