This is a summary of the original PageRank white paper, which can be found at this link.

Abstract

• PageRank is a method 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 user navigation.

1. Introduction and Motivation

• The World Wide Web creates many new challenges for information retrieval. It is very large and heterogeneous. More importantly, the web pages are extremely diverse, ranging from "What is Joe having for lunch today?" to journals about information retrieval.
• The World Wide Web is hypertext and provides considerable auxiliary information on top of the text of the web pages, such as link structure and link text.
• We use PageRank, which helps search engines and users quickly make sense of the vast heterogeneity of the World Wide Web.
  1. Diversity of Web Pages
     1. There are several significant differences between web pages and academic publications.
     2. Unlike academic papers which are scrupulously reviewed, web pages proliferate free of quality control or publishing costs.
     3. the Web environment contains competing for profit-seeking ventures, attention-getting strategies evolve in response to search engine algorithms. For this reason, any evaluation strategy which counts replicable features of web pages is prone to manipulation.
     4. The average web page quality experienced by a user is higher than the quality of the average web page.
  2. PageRank
     1. PageRank is a method for computing a ranking for every web page based on the graph of the web.
     2. PageRank has applications in search, browsing, and traffic estimation.