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Efficient and Accurate SimRank-based Similarity Joins: Experiments, Analysis, and Improvement

Summary: Analyzes SimRank similarity-join methods and gives new theoretical results exposing scalability/accuracy gaps. Proposes approximation‑guaranteed threshold/top‑k frameworks and an efficient randomized local‑push all‑pairs algorithm with complexity bounds and strong empirical gains. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13927
Venue
VLDB
Year
2024
Pagerank
5.2634238e-05
Overall Rank
9,471 | 35.03%
DOI
10.14778/3636218.3636219

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ge_vldb24,
        title = {{Efficient and Accurate SimRank-based Similarity Joins: Experiments, Analysis, and Improvement}},
        author = {Ge, Qian and Liu, Yu and Zhao, Yinghao and Sun, Yuetian and Zou, Lei and Chen, Yuxing and Pan, Anqun},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {4},
        pages = {617--629},
        doi = {10.14778/3636218.3636219},
        url = {https://doi.org/10.14778/3636218.3636219},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
11,237 BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRank 2024 VLDB 5.093636e-05
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