A poster paper by Stanford db group which describes iterative methods for calculating PageRank.

Lawrence Page's PageRank Patent.

It shows some theoretical results for understanding the distribution of the score in the Web according to PageRank. Seven golden rules for building good pages are presented.

A focused search algorithm (SALSA) based on Markov chains. It starts with a query on a broad topic, discards useless links, and then weights the remaining terms. A stochastic crawl is used to discover the authorities on this topic. [PS format]

Information on the algorithm, how to increase PageRank, what diminishes it and how to distribute PageRank within a website.

The CLEVER search engine incorporates several algorithms that make use of hyperlink structure for discovering information on the Web. It is an extension of Hits method.

An eingenvalues algorithm for calculating reputation in P2P networks and isolating malicious peers. There is a relationship with PageRank algorithm.

This method uses query dependent importance scores and a probabilistic approach to improve upon PageRank. It pre-computes importance scores offline for every possible text query.

This paper describes a joint probabilistic model for modeling the contents and inter-connectivity of document collections such as sets of web pages or research paper archives.

First Stanford paper about PageRank. It is a static ranking, performed at indexing time, which interprets a link from page A to page B as a vote, by page A, for page B. Web is seen as a direct graph and votes recursively propagate from nodes to nodes....