On adaptively accelerated Arnoldi method for computing PageRank

Jun Feng Yin, Guo Jian Yin, Kwok Po NG*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

20 Citations (Scopus)


A generalized refined Arnoldi method based on the weighted inner product is presented for computing PageRank. The properties of the generalized refined Arnoldi method were studied. To speed up the convergence performance for computing PageRank, we propose to change the weights adaptively where the weights are calculated based on the current residual corresponding to the approximate PageRank vector. Numerical results show that the proposed Arnoldi method converges faster than existing methods, in particular when the damping factor is large.

Original languageEnglish
Pages (from-to)73-85
Number of pages13
JournalNumerical Linear Algebra with Applications
Issue number1
Publication statusPublished - Jan 2012

Scopus Subject Areas

  • Algebra and Number Theory
  • Applied Mathematics

User-Defined Keywords

  • Arnoldi process
  • Eigenvalue and eigenvector
  • PageRank
  • Power method
  • Weighted least squares problem


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