research-article

Computing personalized PageRank quickly by exploiting graph structures

Authors:

Takanori Maehara,

Ken-ichi KawarabayashiAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 7, Issue 12

Pages 1023 - 1034

https://doi.org/10.14778/2732977.2732978

Published: 01 August 2014 Publication History

Abstract

We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR; however, it requires many iterations. Thus reducing the number of iterations is the main challenge.

We achieve this by exploiting graph structures of web graphs and social networks. The convergence of our algorithm is very fast. In fact, it requires up to 7.5 times fewer iterations than the Power method and is up to five times faster in actual computation time.

To the best of our knowledge, this is the first time to use graph structures explicitly to solve PPR quickly. Our contributions can be summarized as follows.

1. We provide an algorithm for computing a tree decomposition, which is more efficient and scalable than any previous algorithm.

2. Using the above algorithm, we can obtain a core-tree decomposition of any web graph and social network. This allows us to decompose a web graph and a social network into (1) the core, which behaves like an expander graph, and (2) a small tree-width graph, which behaves like a tree in an algorithmic sense.

3. We apply a direct method to the small tree-width graph to construct an LU decomposition.

4. Building on the LU decomposition and using it as pre-conditoner, we apply GMRES method (a state-of-the-art advanced iterative method) to compute PPR for whole web graphs and social networks.

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Cited By

Liao MZhou JLi RDai QChen HWang G(2024)Efficient and Provable Effective Resistance Computation on Large Graphs: An Index-based ApproachProceedings of the ACM on Management of Data10.1145/36549362:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654936
Liao MLi RDai QChen HQin HWang G(2023)Efficient Personalized PageRank Computation: The Power of Variance-Reduced Monte Carlo ApproachesProceedings of the ACM on Management of Data10.1145/35893051:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589305
Hou GGuo QZhang FWang SWei Z(2023)Personalized PageRank on Evolving Graphs with an Incremental Index-Update SchemeProceedings of the ACM on Management of Data10.1145/35887051:1(1-26)Online publication date: 30-May-2023
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Index Terms

Computing personalized PageRank quickly by exploiting graph structures
1. Computing methodologies
  1. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Linear algebra algorithms
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
  2. Mathematical analysis
    1. Numerical analysis
      1. Computations on matrices

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 7, Issue 12

August 2014

296 pages

ISSN:2150-8097

Editors:
H. V. Jagadish
University of Michigan
,
Aoying Zhou
East Normal University, China

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VLDB Endowment

Publication History

Published: 01 August 2014

Published in PVLDB Volume 7, Issue 12

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Cited By

Liao MZhou JLi RDai QChen HWang G(2024)Efficient and Provable Effective Resistance Computation on Large Graphs: An Index-based ApproachProceedings of the ACM on Management of Data10.1145/36549362:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654936
Liao MLi RDai QChen HQin HWang G(2023)Efficient Personalized PageRank Computation: The Power of Variance-Reduced Monte Carlo ApproachesProceedings of the ACM on Management of Data10.1145/35893051:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589305
Hou GGuo QZhang FWang SWei Z(2023)Personalized PageRank on Evolving Graphs with an Incremental Index-Update SchemeProceedings of the ACM on Management of Data10.1145/35887051:1(1-26)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588705
Zhang JLi WYuan LQin LZhang YChang L(2022)Shortest-path queries on complex networksProceedings of the VLDB Endowment10.14778/3551793.355182015:11(2640-2652)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.14778/3551793.3551820
Liao MLi RDai QWang GIves ZBonifati AEl Abbadi A(2022)Efficient Personalized PageRank Computation: A Spanning Forests Sampling Based ApproachProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526140(2048-2061)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526140
Hou GChen XWang SWei Z(2021)Massively parallel algorithms for personalized pagerankProceedings of the VLDB Endowment10.14778/3461535.346155414:9(1668-1680)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.14778/3461535.3461554
Mo DLuo SDemartini GZuccon GCulpepper JHuang ZTong H(2021)AgendaProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482317(1315-1324)Online publication date: 26-Oct-2021
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Wu HGan JWei ZZhang RLi GLi ZIdreos SSrivastava D(2021)Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward PushProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457298(1996-2008)Online publication date: 9-Jun-2021
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Wang HWei ZGan JWang SHuang ZGupta RLiu YShah MRajan STang JPrakash B(2020)Personalized PageRank to a Target Node, RevisitedProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403108(657-667)Online publication date: 23-Aug-2020
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Li WQiao MQin LZhang YChang LLin XMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Scaling Up Distance Labeling on Graphs with Core-Periphery PropertiesProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3389748(1367-1381)Online publication date: 11-Jun-2020
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