This paper discusses ecient techniques for computing PageRank, a ranking met-
ric 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
con gurations are presented for PageRank computation over the 24-million-page
Stanford WebBase archive. We discuss several methods for analyzing the con-
vergence 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.
