Postscript-format slides which introduces citation importance ranking by Larry Page, Google's founder.

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....

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.

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]

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.

A paper about the computation of PageRank using the standard Power Method and the new Quadratic Extrapolation which computes the principal eigenvector of the Markov matrix representing the Web link graph with an increased speed up of about 50-300%.

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

This paper describes a prototype system, later known as the Teoma Search Engine. It performs a Link Analysis, loosely based on the Kleimberg method, and computed at query time.

A good explanation about the convergence of various algorithms. This paper also describes an adaptive and on-line algorithm for computing the page importance. It can be used for focus crawling as well as for search engine's ranking.

PageRank and Hub and Authority generalization based on the topic of Web Pages. Definition of a model where a surfer can move forward (following an out-going link) and backward (following an in-going link in the inverse direction). [PS format]