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

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.

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 presents an algorithm to vastly speed up the computation of PageRank.

Paper by Junghoo Cho, Hector Garcia-Molina, and Lawrence Page. Available in Postscript, PDF, and plain text formats. [PDF]

Paper by Sergey Brin and Lawrence Page, available in Postscript, PDF, and plain text formats.

PhD thesis by Kahn Mason on methods of discovering groups of websites that collude to boost their reputations, distorting the results of the PageRank algorithm. Stanford University.

Paper by Min Fang, Narayanan Shivakumar, Hector Garcia-Molina, Rajeev Motwani, and Jeffrey D. Ullman, developing efficient execution strategies for a class of queries which perform an aggregate function over an attribute (or set of attributes) and the...

Paper from WWW10 by Sergey Melnik, Sriram Raghavan, Beverly Yang, Hector Garcia-Molina from the Computer Science Department at Stanford University.

Taher H. Haveliwala, Sepandar D. Kamvar, and Glen Jeh compare three approaches to personaliizing PageRank.