How to make the top ten: Approximating PageRank from in-degree

TL;DR

This paper investigates how well PageRank can be approximated using the local measure of in-degree, combining theoretical and empirical analysis to assess its accuracy given the Web's link structure.

Contribution

It provides a novel analysis demonstrating that in-degree can serve as a relatively accurate proxy for PageRank due to weak degree correlations in the Web graph.

Findings

In-degree approximates PageRank effectively in Web graphs.

Weak degree correlations enable local measures to predict global importance.

The approach offers a practical tool for service providers to estimate PageRank.

Abstract

PageRank has become a key element in the success of search engines, allowing to rank the most important hits in the top 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 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 to conclude that given the weak degree correlations in the Web link graph, the approximation can be relatively accurate, giving service and information providers an effective new marketing tool.