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An Inner/Outer Stationary Iteration for Computing PageRank

Authors: Andrew P. Gray, Chen Greif, and Tracy Lau

Published in: Dagstuhl Seminar Proceedings, Volume 7071, Web Information Retrieval and Linear Algebra Algorithms (2007)

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.

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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)

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@InProceedings{gray_et_al:DagSemProc.07071.5,
  author =	{Gray, Andrew P. and Greif, Chen and Lau, Tracy},
  title =	{{An Inner/Outer Stationary Iteration for Computing PageRank}},
  booktitle =	{Web Information Retrieval and Linear Algebra Algorithms},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7071},
  editor =	{Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07071.5},
  URN =		{urn:nbn:de:0030-drops-10628},
  doi =		{10.4230/DagSemProc.07071.5},
  annote =	{Keywords: PageRank, power method, stationary method, inner/outer iterations, damping factor}
}

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DOI: 10.4230/DagSemProc.07071.17

Three results on the PageRank vector: eigenstructure, sensitivity, and the derivative

Authors: David Gleich, Peter Glynn, Gene Golub, and Chen Greif

Published in: Dagstuhl Seminar Proceedings, Volume 7071, Web Information Retrieval and Linear Algebra Algorithms (2007)

Abstract

The three results on the PageRank vector are preliminary but shed light on the eigenstructure of a PageRank modified Markov chain and what happens when changing the teleportation parameter in the PageRank model. Computations with the derivative of the PageRank vector with respect to the teleportation parameter show predictive ability and identify an interesting set of pages from Wikipedia.

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David Gleich, Peter Glynn, Gene Golub, and Chen Greif. Three results on the PageRank vector: eigenstructure, sensitivity, and the derivative. In Web Information Retrieval and Linear Algebra Algorithms. Dagstuhl Seminar Proceedings, Volume 7071, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{gleich_et_al:DagSemProc.07071.17,
  author =	{Gleich, David and Glynn, Peter and Golub, Gene and Greif, Chen},
  title =	{{Three results on the PageRank vector:  eigenstructure, sensitivity, and the derivative}},
  booktitle =	{Web Information Retrieval and Linear Algebra Algorithms},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7071},
  editor =	{Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07071.17},
  URN =		{urn:nbn:de:0030-drops-10615},
  doi =		{10.4230/DagSemProc.07071.17},
  annote =	{Keywords: PageRank, PageRank derivative, PageRank sensitivity, PageRank eigenstructure}
}

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An Inner/Outer Stationary Iteration for Computing PageRank

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Three results on the PageRank vector: eigenstructure, sensitivity, and the derivative

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