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One Index for All: Towards Efficient Personalized PageRank Computation for Every Damping Factor

Summary: Introduces StackIndex: a single stack-style meta-index built via loop-erased α-random walks with a large ᾱ that can be efficiently transformed to answer SSPPR for any damping factor α without rebuilding. Provably O(ω n) time/space construction, supports dynamic updates and yields large empirical speedups. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7523
Venue
SIGMOD
Year
2026
Pagerank
5.3251649e-05
Overall Rank
9,046 | 37.94%
DOI
10.1145/3749176

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhou_sigmod26,
        title = {{One Index for All: Towards Efficient Personalized PageRank Computation for Every Damping Factor}},
        author = {Zhou, Junjie and Liao, Meihao and Li, Rong-Hua and Lin, Longlong and Wang, Guoren},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3749176},
        url = {https://dl.acm.org/doi/10.1145/3749176},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
10,161 Near-Optimality for Single-Source Personalized PageRank 2026 PODS 5.093636e-05
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