title: Efficient Computation of PageRank
creator: Haveliwala, T.
subject: Data Mining
description: Efficient Computation of PageRank Taher H. Haveliwala (taherh@db.stanford.edu)  Abstract: This paper discusses efficient techniques for computing PageRank, a ranking metric for hypertext documents.  We show that PageRank can be computed for very large subgraphs of the web (up to hundreds of millions of nodes) on machines with limited main memory.  Running-time measurements on various memory configurations are presented for PageRank computation over the 24-million-page Stanford WebBase archive.  We discuss several methods for analyzing the convergence of PageRank based on the induced ordering of the pages.  We present convergence results helpful for determining the number of iterations necessary to achieve a useful PageRank assignment, both in the absence and presence of search queries.
publisher: Stanford
date: 1999
type: Techreport
type: NonPeerReviewed
format: application/pdf
identifier: http://ilpubs.stanford.edu:8090/386/1/1999-31.pdf
identifier: Haveliwala, T. (1999) Efficient Computation of PageRank. Technical Report. Stanford.
relation: http://ilpubs.stanford.edu:8090/386/