Paper by Sergey Brin presenting a technique which exploits the duality between sets of patterns and relations to grow the target relation, starting from a small sample.

This paper by Sepandar Kamvar, Taher Haveliwala, Chris Manning, and Gene Golub, published in WWW13, presents an algorithm to speed up the computation of PageRank by making some initial approximations.

By Narayanan Shivakumar and Hector Garcia-Molina. Available in Postscript format.

United States Patent 7, 058, 628, granted to Lawrence Page, which incorporates material from two earlier patents relating to the PageRank system used by Google.

Paper by T. Haveliwala, describing efficient techniques for computing PageRank.

Google supplies a partial list of papers written by people now at Google.

The definitive paper by Sergey Brin and Lawrence Page describing PageRank, the algorithm that was later incorporated into the Google search engine.

Terrence A. Brooks writes a paper about how search engines are changing the way we understand the world around us.

Stanford paper by Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd, describing PageRank as a static ranking, performed at indexing time, which interprets a link as a vote. Available in Postscript, PDF, and plain text formats.

This paper by Sepandar Kamvar and Taher Haveliwala proves analytically the second eigenvalue of the Google Matrix, which has implications for the PageRank algorithm.