Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach
arxiv(2023)
摘要
Subgraph counting is a fundamental problem in understanding and analyzing
graph structured data, yet computationally challenging. This calls for an
accurate and efficient algorithm for Subgraph Cardinality Estimation, which is
to estimate the number of all isomorphic embeddings of a query graph in a data
graph. We present FaSTest, a novel algorithm that combines (1) a powerful
filtering technique to significantly reduce the sample space, (2) an adaptive
tree sampling algorithm for accurate and efficient estimation, and (3) a
worst-case optimal stratified graph sampling algorithm for difficult instances.
Extensive experiments on real-world datasets show that FaSTest outperforms
state-of-the-art sampling-based methods by up to two orders of magnitude and
GNN-based methods by up to three orders of magnitude in terms of accuracy.
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