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Strong Localization in Personalized PageRank Vectors

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Algorithms and Models for the Web Graph (WAW 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9479))

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Abstract

The personalized PageRank diffusion is a fundamental tool in network analysis tasks like community detection and link prediction. It models the spread of a quantity from a set of seed nodes, and it has been observed to stay localized near this seed set. We derive an upper-bound on the number of entries necessary to approximate a personalized PageRank vector in graphs with skewed degree sequences. This bound shows localization under mild assumptions on the maximum and minimum degrees. Experimental results on random graphs with these degree sequences show the bound is loose and support a conjectured bound.

K. Kloster and D.F. Gleich—Supported by NSF CAREER award CCF-1149756 and DARPA SIMPLEX Code available online https://github.com/nassarhuda/pprlocal.

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Author information

Authors and Affiliations

  1. Computer Science Department, Purdue University, West Lafayette, USA

    Huda Nassar & David F. Gleich

  2. Mathematics Department, Purdue University, West Lafayette, USA

    Kyle Kloster

Authors
  1. Huda Nassar
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  2. Kyle Kloster
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  3. David F. Gleich
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Corresponding author

Correspondence to Huda Nassar .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, Indiana, USA

    David F. Gleich

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Júlia Komjáthy

  3. University of Twente, Enschede, The Netherlands

    Nelly Litvak

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Nassar, H., Kloster, K., Gleich, D.F. (2015). Strong Localization in Personalized PageRank Vectors. In: Gleich, D., Komjáthy, J., Litvak, N. (eds) Algorithms and Models for the Web Graph. WAW 2015. Lecture Notes in Computer Science(), vol 9479. Springer, Cham. https://doi.org/10.1007/978-3-319-26784-5_15

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  • DOI: https://doi.org/10.1007/978-3-319-26784-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26783-8

  • Online ISBN: 978-3-319-26784-5

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