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Estimating Single-Node PageRank in Õ (min{d_t, √m}) Time

Authors:

Zhewei WeiAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 16, Issue 11

Pages 2949 - 2961

https://doi.org/10.14778/3611479.3611500

Published: 01 July 2023 Publication History

Abstract

PageRank is a famous measure of graph centrality that has numerous applications in practice. The problem of computing a single node's PageRank has been the subject of extensive research over a decade. However, existing methods still incur large time complexities despite years of efforts. Even on undirected graphs where several valuable properties held by PageRank scores, the problem of locally approximating the PageRank score of a target node remains a challenging task. Two commonly adopted techniques, Monte-Carlo based random walks and backward push, both cost O(n) time in the worst-case scenario, which hinders existing methods from achieving a sublinear time complexity like O(√m) on an undirected graph with n nodes and m edges.

In this paper, we focus on the problem of single-node PageRank computation on undirected graphs. We propose a novel algorithm, SetPush, for estimating single-node PageRank specifically on undirected graphs. With non-trival analysis, we prove that our SetPush achieves the Õ (min {d_t, √m]}) time complexity for estimating the target node t's PageRank with constant relative error and constant failure probability on undirected graphs. We conduct comprehensive experiments to demonstrate the effectiveness of SetPush.

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Cited By

Liu HLuo S(2024)BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRankProceedings of the VLDB Endowment10.14778/3665844.366585517:9(2255-2268)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665855
Wang HBaeza-Yates RBonchi F(2024)Revisiting Local PageRank Estimation on Undirected Graphs: Simple and OptimalProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671820(3036-3044)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671820
Yang MWang HWei ZWang SWen J(2024)Efficient Algorithms for Personalized PageRank Computation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337600036:9(4582-4602)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3376000

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Information & Contributors

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 16, Issue 11

July 2023

789 pages

ISSN:2150-8097

Editors:
Georgia Koutrika
Athena Research Center
,
Jun Yang
Duke University

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VLDB Endowment

Publication History

Published: 01 July 2023

Published in PVLDB Volume 16, Issue 11

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Cited By

Liu HLuo S(2024)BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRankProceedings of the VLDB Endowment10.14778/3665844.366585517:9(2255-2268)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665855
Wang HBaeza-Yates RBonchi F(2024)Revisiting Local PageRank Estimation on Undirected Graphs: Simple and OptimalProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671820(3036-3044)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671820
Yang MWang HWei ZWang SWen J(2024)Efficient Algorithms for Personalized PageRank Computation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337600036:9(4582-4602)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3376000

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