Presentation paper. Link Popularity algorithms biased according to a user-specified set of given interesting pages.

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]

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

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

"Random Surfer" model extension. At each step of traversal of the Web graph, the surfer can jump to a random node or follow a hyperlink or follow a back-link (a hyperlink in the inverse direction) or stay in the same node.

Integrates ODP data in PageRank calculation for performing query time probabilistic ranking.

"Google's PageRank is an eigenvector of a matrix of order 2.7 billion"

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 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.

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.