On Local Estimations of PageRank: A Mean Field Approach

Abstract

PageRank is a key element in the success of search engines, allowing the display of the most relevant hits in the first screen of results. One key aspect that distinguishes PageRank from other prestige measures such as in-degree is its global nature. From the information provider perspective, this makes it difficult or even impossible to predict how their pages will be ranked. Consequently, a market has emerged for the optimization of search engine results. Here we study the accuracy with which PageRank can be approximated by in-degree, a local measure made freely available by search engines. Theoretical and empirical analyses lead us to conclude that, given the weak degree of correlations in the Web link graph, the approximation can be relatively accurate, giving service and information providers an effective new marketing tool.