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Available viahttp://dbpubs.stanford.edu/pub/2003-17
Submitted on 4th of March 2003
Author Kamvar, Sepandar; Haveliwala, Taher; Manning, Christopher; Golub, Gene
Title Exploiting the Block Structure of the Web for Computing PageRank
Date of publication 2003
Published in Stanford University Technical Report
Citation Kamvar, Sepandar; Haveliwala, Taher; Manning, Christopher; Golub, Gene. Exploiting the Block Structure of the Web for Computing PageRank, Stanford University Technical Report, 2003.
Number of pages 13
Language English
Project Stanford InfoLab; Database Group; Natural Language Processing Group
Type Technical Report
Subject group Computer Science; Databases and the Web; Miscellaneous
Abstract The web link graph has a nested block structure: the vast majority of hyperlinks link pages on a host to other pages on the same host, and many of those that do not link pages within the same domain. We show how to exploit this structure to speed up the computation of PageRank by a 3-stage algorithm whereby (1)~the local PageRanks of pages for each host are computed independently using the link structure of that host, (2)~these local PageRanks are then weighted by the ``importance'' of the corresponding host, and (3)~the standard PageRank algorithm is then run using as its starting vector the weighted aggregate of the local PageRanks. Empirically, this algorithm speeds up the computation of PageRank by a factor of 2 in realistic scenarios. Further, we develop a variant of this algorithm that efficiently computes many different ``personalized'' PageRanks, and a variant that efficiently recomputes PageRank after node updates.
Keywords PageRank, link analysis, eigenvector, markov chain
Contact address sdkamvar@stanford.edu
taherh@cs.stanford.edu
Sponsored by This paper is based on work supported in part by the National
Science Foundation under Grant No.\ IIS-0085896 and Grant No.\ CCR-9971010, and
in part by the Research Collaboration
between NTT Communication Science Laboratories, Nippon Telegraph and
Telephone Corporation and CSLI, Stanford University (research project
on Concept Bases for Lexical Acquisition and Intelligently Reasoning
with Meaning).
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