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Efficient Personalized PageRank Computation: A Spanning Forests Sampling Based Approach

Summary: Introduces spanning-forests sampling for personalized PageRank via loop-erased alpha-random walks, grounded in a new matrix forest theorem. Alpha-insensitive, fast single-source and single-target PPR queries; outperforms prior methods with experiments on seven large real-world graphs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6514
Venue
SIGMOD
Year
2022
Pagerank
5.927015e-05
Overall Rank
6,284 | 56.89%
DOI
10.1145/3514221.3526140

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{liao_sigmod22,
        title = {{Efficient Personalized PageRank Computation: A Spanning Forests Sampling Based Approach}},
        author = {Liao, Meihao and Li, Rong-Hua and Dai, Qiangqiang and Wang, Guoren},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3526140},
        url = {https://dl.acm.org/doi/10.1145/3514221.3526140},
        year = {2022}
}

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