Title: Survey: Eigenvector Analysis in Webpage Rankings 

(NYU-CS-TR835)

Author: Hung-Hsien Chang 

Abstract:
Two major techniques have been proposed for using the structure
of links in the World Wide Web to determine the relative significance
of Web Pages.  The PageRank algorithm \cite{BP98}, which is a critical
part of the Google search engine, gives a single measure of importance
of each page in the Web.  The HITS algorithm \cite{K98} applies
to a set of pages believed relevant to a given query, and assigns
two values to each page: the degree to which the page is a hub and
the degree to which it is an authority.  Both algorithms have a
natural interpretation in terms of a random walk over the set
of pages involved, and in both cases the computation involved amounts
to computing an eigenvector over the transition matrix for this random
walk.
                                                                                                                             
This paper surveys the literature discussing these two techniques
and their variants, and their connection to random walks and
eigenvector computation.  It also discusses the stability of
these techniques under small changes in the Web link structure.