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AGIS: Fast Approximate Graph Pattern Mining with Structure-Informed Sampling

Summary: AGIS replaces uniform sampling with pattern-dependent, structure-informed neighbor sampling for fast approximate counting of arbitrary graph motifs. Its adaptive distribution selection delivers up to 28.5× average speedups and scales to tens of billions of edges. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14515
Venue
VLDB
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,575 | 27.45%
DOI
10.14778/3773749.3773761

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BibTeX Citation

@article{lee_vldb26,
        title = {{AGIS: Fast Approximate Graph Pattern Mining with Structure-Informed Sampling}},
        author = {Lee, Seoyong and Lee, Jinho},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {2},
        pages = {238--251},
        doi = {10.14778/3773749.3773761},
        url = {https://doi.org/10.14778/3773749.3773761},
        year = {2026}
}

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