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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
6452
Venue
SIGMOD
Year
2022
Pagerank
4.8381004e-05
Overall Rank
7,086 | 50.71%
DOI
10.1145/3514221.3526140

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