The PageRank Citation Ranking: Bringing Order to the Web

Authors: Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd

Published: 1998 (Technical Report)

Source: Stanford InfoLab

Algorithm: PageRank

Summary

This technical report introduces PageRank, a link-analysis ranking method that scores web pages by the stationary behavior of an idealized random surfer on the web graph. Its main contribution is to turn hyperlink structure into a scalable global importance signal for search and navigation, making it one of the canonical bridges between eigenvector-style centrality, Markov chains, and web-scale information retrieval.

Abstract

The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

Links

Primary

Paper ilpubs.stanford.edu:8090

Alternate

ilpubs.stanford.edu ilpubs.stanford.edu

Tags

• Link analysis

• Web search

• Graph centrality

• Eigenvector centrality

• Random walk

• Markov chain

• Information retrieval

• Citation ranking

• Ranking algorithms

• Web graph