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...

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

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.

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

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

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

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.

Paper by Minhua Wang describing an improvement to Kleimberg's algorithm.

It proposes a new weighted HITS-based method that assigns appropriate weights to in-links of root documents and combines content analysis with HITS-based algorithms.

"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.