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An Inner/Outer Stationary Iteration for Computing PageRank

Authors Andrew P. Gray, Chen Greif, Tracy Lau



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Andrew P. Gray
Chen Greif
Tracy Lau

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Andrew P. Gray, Chen Greif, and Tracy Lau. An Inner/Outer Stationary Iteration for Computing PageRank. In Web Information Retrieval and Linear Algebra Algorithms. Dagstuhl Seminar Proceedings, Volume 7071, pp. 1-8, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2007)
https://doi.org/10.4230/DagSemProc.07071.5

Abstract

We present a stationary iterative scheme for PageRank computation. The algorithm is based on a linear system formulation of the problem, uses inner/outer iterations, and amounts to a simple preconditioning technique. It is simple, can be easily implemented and parallelized, and requires minimal storage overhead. Convergence analysis shows that the algorithm is effective for a crude inner tolerance and is not particularly sensitive to the choice of the parameters involved. Numerical examples featuring matrices of dimensions up to approximately $10^7$ confirm the analytical results and demonstrate the accelerated convergence of the algorithm compared to the power method.
Keywords
  • PageRank
  • power method
  • stationary method
  • inner/outer iterations
  • damping factor

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