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Efficient pagerank approximation via graph aggregation

Published:19 May 2004Publication History

ABSTRACT

We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. We (1) partition the Web's graph into classes of quasi-equivalent vertices, (2) project the page-based random walk to be approximated onto those classes, and (3) compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. Inparticular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. We experimented on a Web-graph containing over 1.4 billion pages, and were able to produce a ranking that has Spearman rank-order correlation of 0.95 with respect to PageRank. A simplistic implementation of our method required less than half the running time of a highly optimized implementation of PageRank, implying that larger speedup factors are probably possible.

References

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  2. T. H. Haveliwala. Efficient computation of pagerank. Technical Report Technical Report, Stanford University, October 1999.Google ScholarGoogle Scholar
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    • Published in

      cover image ACM Conferences
      WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
      May 2004
      532 pages
      ISBN:1581139128
      DOI:10.1145/1013367

      Copyright © 2004 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 19 May 2004

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