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Computer science Graph Sampling (signal processing) Theoretical computer science Data mining Artificial intelligence Computer vision Filter (signal processing)

Peng Jiang , Yihua Wei , Jiya Su , Rujia Wang , Bo Wu ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1145/3559009.3569658 · OA: W4320063585

Subgraph Pattern Mining (SPM) is an important class of graph applications that aim to discover structural patterns in a graph. Due to the enormous exploration space, SPM is in general computationally challenging. To accelerate SPM, many random sampling techniques have been proposed. While the existing sampling techniques are effective for conventional SPM tasks such as motif counting and frequent subgraph mining, they cannot be easily adapted for new applications.