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.