HITs is a link-structure analysis algorithm which ranks pages by "authorities" (pages which have many incoming links and provide the best source of information on a given topic) and "hubs" (pages which have many outgoing links and provide useful lists of possibly relevant pages). Ranking is performed at query time. [PDF]

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. Ranking is performed at indexing time. Used by Google.

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. [PDF]

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. [PDF]

Lossy encoding for large scale PageRank calculation. [PDF]

Lawrence Page's PageRank Patent.

About the using of PageRank in Web Track 8 "large" and "small" datasets. [PDF]

About the using of Link Popularity in Web Track 9 datasets. [PDF]

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]