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SimTab: Accuracy-Guaranteed SimRank Queries through Tighter Confidence Bounds and Multi-Armed Bandits

Summary: SimTab unifies top-k and threshold SimRank with the first accuracy guarantees, combining tighter random-walk confidence bounds and multi-armed bandits. Its index-free, graph-tailored sampling scales to dynamic large graphs and improves theoretical and empirical efficiency. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12297
Venue
VLDB
Year
2020
Pagerank
5.093636e-05
Overall Rank
11,796 | 19.07%
DOI
10.14778/3407790.3407819

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Authors

BibTeX Citation

@article{liu_vldb20,
        title = {{SimTab: Accuracy-Guaranteed SimRank Queries through Tighter Confidence Bounds and Multi-Armed Bandits}},
        author = {Liu, Yu and Zou, Lei and Ge, Qian and Wei, Zhewei},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2202--2214},
        doi = {10.14778/3407790.3407819},
        url = {https://doi.org/10.14778/3407790.3407819},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
9,471 Efficient and Accurate SimRank-based Similarity Joins: Experiments, Analysis, and Improvement 2024 VLDB 5.2634238e-05
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