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)
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Authors
- 1. Meihao Liao (Beijing Institute of Technology)
- 2. Rong-Hua Li (Beijing Institute of Technology)
- 3. Qiangqiang Dai (Beijing Institute of Technology)
- 4. Guoren Wang (Beijing Institute of Technology)
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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