Efficient Parallel Computation of PageRank

  • Christian Kohlschütter
  • Paul-Alexandru Chirita
  • Wolfgang Nejdl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


PageRank inherently is massively parallelizable and distributable, as a result of web’s strict host-based link locality. We show that the Gauß-Seidel iterative method can actually be applied in such a parallel ranking scenario in order to improve convergence. By introducing a two-dimensional web model and by adapting the PageRank to this environment, we present efficient methods to compute the exact rank vector even for large-scale web graphs in only a few minutes and iteration steps, with intrinsic support for incremental web crawling, and without the need for page sorting/reordering or for sharing global rank information.


Link Structure Outgoing Link PageRank Algorithm Jacobi Iteration Target Page 
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 2006

Authors and Affiliations

  • Christian Kohlschütter
    • 1
  • Paul-Alexandru Chirita
    • 1
  • Wolfgang Nejdl
    • 1
  1. 1.L3S Research Center/University of HanoverHanoverGermany

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