Dynamic PageRank Using Evolving Teleportation

  • Ryan A. Rossi
  • David F. Gleich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7323)

Abstract

The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an evolving teleportation adaptation of the PageRank method to capture how changes in external interest influence the importance of a node. This framework seamlessly generalizes PageRank because the importance of a node will converge to the PageRank values if the external influence stops changing. We demonstrate the effectiveness of the evolving teleportation on the Wikipedia graph and the Twitter social network. The external interest is given by the number of hourly visitors to each page and the number of monthly tweets for each user.

Keywords

Dynamic Graph Page Count Forward Euler Method PageRank Vector PageRank Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ryan A. Rossi
    • 1
  • David F. Gleich
    • 1
  1. 1.Department of Computer SciencePurdue UniversityWest LafayetteUSA

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