The powerrank web link analysis algorithm

  • Authors:
  • Yizhou Lu;Benyu Zhang;Wensi Xi;Zheng Chen;Yi Liu;Michael R. Lyu;Wei-ying Ma

  • Affiliations:
  • Peking University, Beijing, China;Microsoft Research Asia, Beijing, P. R. China;Virginia Polytechnic Institute and State University, Blacksburg, VA;Microsoft Research Asia, Beijing, P. R. China;The Chinese University of Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong;Microsoft Research Asia, Beijing, P. R. China

  • Venue:
  • Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
  • Year:
  • 2004

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Abstract

The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm "the PowerRank algorithm", which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low "importance" score. In the second part, the global "importance" score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10%-30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm.