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Efficient Algorithms for Finding Approximate Heavy Hitters in Personalized PageRanks

Summary: BLOG targets heavy-hitter PPR queries: pairwise AHH, reverse AHH, and multi-source reverse AHH. By combining Monte-Carlo sampling with backward propagation and logarithmic bucketing to handle high in-degree nodes, it beats SOTA on all three queries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5627
Venue
SIGMOD
Year
2018
Pagerank
6.1029326e-05
Overall Rank
5,741 | 60.62%
DOI
10.1145/3183713.3196919

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod18,
        title = {{Efficient Algorithms for Finding Approximate Heavy Hitters in Personalized PageRanks}},
        author = {Wang, Sibo and Tao, Yufei},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3196919},
        url = {https://dl.acm.org/doi/10.1145/3183713.3196919},
        year = {2018}
}

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