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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
- 13913
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,640 | 26.06%
- 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 |
| 460 |
Graphs-at-a-time: Query Language and Access Methods for Graph Databases |
2008 |
SIGMOD |
0.00022679846 |
| 610 |
Design and Implementation of the LogicBlox System |
2015 |
SIGMOD |
0.00019204048 |
| 616 |
Taming Verification Hardness: An Efficient Algorithm for Testing Subgraph Isomorphism |
2008 |
VLDB |
0.00019068362 |
| 749 |
TurboISO: Towards UltraFast and Robust Subgraph Isomorphism Search in Large Graph Databases |
2013 |
SIGMOD |
0.00017193776 |
| 941 |
Wander Join: Online Aggregation via Random Walks |
2016 |
SIGMOD |
0.00015147831 |
| 964 |
G-CORE: A Core for Future Graph Query Languages |
2018 |
SIGMOD |
0.00014967208 |
| 1,125 |
Efficient Subgraph Matching by Postponing Cartesian Products |
2016 |
SIGMOD |
0.00013829006 |
| 1,372 |
Random Sampling over Joins Revisited |
2018 |
SIGMOD |
0.0001233325 |
| 1,522 |
Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together |
2019 |
SIGMOD |
0.0001152219 |
| 1,715 |
CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching |
2019 |
SIGMOD |
0.00010776518 |
| 1,906 |
In-Memory Subgraph Matching: An In-depth Study |
2020 |
SIGMOD |
0.00010135267 |
| 2,143 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
9.4437798e-05 |
| 2,172 |
Scalable Subgraph Enumeration in MapReduce |
2015 |
VLDB |
9.37776e-05 |
| 2,787 |
Scalable Distributed Subgraph Enumeration |
2017 |
VLDB |
8.1219297e-05 |
| 2,988 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
7.7752463e-05 |
| 3,034 |
RapidMatch: A Holistic Approach to Subgraph Query Processing |
2021 |
VLDB |
7.6737281e-05 |
| 3,119 |
Versatile Equivalences: Speeding up Subgraph Query Processing and Subgraph Matching |
2021 |
SIGMOD |
7.5393376e-05 |
| 3,412 |
Motivo: fast motif counting via succinct color coding and adaptive sampling |
2019 |
VLDB |
7.1194524e-05 |
| 3,781 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
6.7691344e-05 |
| 4,326 |
GuP: Fast Subgraph Matching by Guard-based Pruning |
2023 |
SIGMOD |
6.2772512e-05 |
| 5,002 |
HUGE: An Efficient and Scalable Subgraph Enumeration System |
2021 |
SIGMOD |
5.7610359e-05 |
| 5,968 |
A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction |
2024 |
SIGMOD |
5.2469955e-05 |
| 6,259 |
Neural Attributed Community Search at Billion Scale |
2023 |
SIGMOD |
5.1305787e-05 |
| 6,283 |
Fast Continuous Subgraph Matching over Streaming Graphs via Backtracking Reduction |
2023 |
SIGMOD |
5.1234789e-05 |
| 6,288 |
Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach |
2024 |
VLDB |
5.1226099e-05 |
| 6,436 |
Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding |
2024 |
VLDB |
5.0554554e-05 |
| 6,705 |
Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation |
2021 |
SIGMOD |
4.9507418e-05 |
| 9,729 |
TC-Match: Fast Time-constrained Continuous Subgraph Matching |
2024 |
VLDB |
4.2901665e-05 |
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| 3,781 |
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| 2,988 |
Neural Subgraph Counting with Wasserstein Estimator |
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SIGMOD |
7.7752463e-05 |
| 7,936 |
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2024 |
VLDB |
4.6089395e-05 |