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Personalized PageRank with Node-Dependent Restart

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Algorithms and Models for the Web Graph (WAW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8882))

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Abstract

Personalized PageRank is an algorithm to classify the importance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with node-dependent restart. The first generalization is based on the proportion of visits to nodes before the restart, whereas the second generalization is based on the proportion of time a node is visited just before the restart. In the original case of constant restart probability, the two measures coincide. We discuss interesting particular cases of restart probabilities and restart distributions. We show that both generalizations of Personalized PageRank have an elegant expression connecting the so-called direct and reverse Personalized PageRanks that yield a symmetry property of these Personalized PageRanks.

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Author information

Authors and Affiliations

  1. Inria Sophia Antipolis, Valbonne, France

    Konstantin Avrachenkov & Marina Sokol

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Remco van der Hofstad

Authors
  1. Konstantin Avrachenkov
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  2. Remco van der Hofstad
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  3. Marina Sokol
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Corresponding author

Correspondence to Konstantin Avrachenkov .

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, Ontario, Canada

    Anthony Bonato

  2. University of California San Diego, La Jolla, California, USA

    Fan Chung Graham

  3. Ryerson University, Toronto, Ontario, Canada

    Paweł Prałat

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Avrachenkov, K., van der Hofstad, R., Sokol, M. (2014). Personalized PageRank with Node-Dependent Restart. In: Bonato, A., Graham, F., Prałat, P. (eds) Algorithms and Models for the Web Graph. WAW 2014. Lecture Notes in Computer Science(), vol 8882. Springer, Cham. https://doi.org/10.1007/978-3-319-13123-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-13123-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13122-1

  • Online ISBN: 978-3-319-13123-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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