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A Case Study in Web Search using TREC Algorithms - http://www10.org/cdrom/papers/317/
Paper from WWW10 by Google employees Amit Singhal and Marcin Kaszkiel.

Adaptive Methods for the Computation of PageRank - http://www.stanford.edu/~sdkamvar/papers/adaptive.pdf
This paper by Sepandar Kamvar, Taher Haveliwala, and Gene Golub describes an algorithm to speed up the computation of PageRank using the fact that pages converge at different rates.

An Analysis of Factors Used in Search Engine Ranking - http://airweb.cse.lehigh.edu/2005/bifet.pdf
Investigates the influence of different page features on the ranking of Google search engine results.

An Analytical Comparison of Approaches to Personalizing PageRank - http://www.stanford.edu/~sdkamvar/papers/comparison.pdf
Taher H. Haveliwala, Sepandar D. Kamvar, and Glen Jeh compare three approaches to personaliizing PageRank.

Building a Distributed Full-Text Index for the Web - http://www10.org/cdrom/papers/275/
Paper from WWW10 by Sergey Melnik, Sriram Raghavan, Beverly Yang, Hector Garcia-Molina from the Computer Science Department at Stanford University.

Computing Iceberg Queries Efficiently - http://www.vldb.org/conf/1998/p299.pdf
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 then eliminates aggregate values that are below some specified threshold.

Detecting Colluders in PageRank - http://www.stanford.edu/group/reputation/Mason_Thesis.pdf
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.

Dynamic Data Mining: Exploring Large Rule Spaces by Sampling - http://ilpubs.stanford.edu:8090/424/
Paper by Sergey Brin and Lawrence Page, available in Postscript, PDF, and plain text formats.

Efficient Crawling Through URL Ordering - http://ilpubs.stanford.edu:8090/347/
Paper by Junghoo Cho, Hector Garcia-Molina, and Lawrence Page. Available in Postscript, PDF, and plain text formats. [PDF]

Exploiting the Block Structure of the Web for Computing PageRank - http://www.stanford.edu/~sdkamvar/papers/blockrank.pdf
This paper by Sepandar Kamvar, Taher Haveliwala, Chris Manning, and Gene Golub presents an algorithm to vastly speed up the computation of PageRank.

Extracting Patterns and Relations from the World Wide Web - http://maya.cs.depaul.edu/~classes/ect584/papers/brin.pdf
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.

Extrapolation Methods for Accelerating PageRank Computations - http://www.stanford.edu/~sdkamvar/papers/extrapolation.pdf
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.

Finding Near-replicas of Documents on the Web - http://infolab.stanford.edu/~shiva/Pubs/web.ps
By Narayanan Shivakumar and Hector Garcia-Molina. Available in Postscript format.

Method for Node Ranking in a Linked Database - http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=7,058,628.PN.&OS=PN/7,058,628&RS=PN/7,058,628
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.

PageRank Calculation Techniques - http://www-cs-students.stanford.edu/~taherh/papers/efficient-pr.pdf
Paper by T. Haveliwala, describing efficient techniques for computing PageRank.

Papers by Googlers - http://research.google.com/pubs/papers.html
Google supplies a partial list of papers written by people now at Google.

The Anatomy of a Large-Scale Hypertextual Web Search Engine - http://infolab.stanford.edu/~backrub/google.html
The definitive paper by Sergey Brin and Lawrence Page describing PageRank, the algorithm that was later incorporated into the Google search engine.

The Nature of Meaning in the Age of Google - http://informationr.net/ir/9-3/paper180.html
Terrence A. Brooks writes a paper about how search engines are changing the way we understand the world around us.

The PageRank Citation Ranking: Bringing Order to the Web - http://ilpubs.stanford.edu:8090/422/
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.

The Second Eigenvalue of the Google Matrix - http://www.stanford.edu/~sdkamvar/papers/secondeigenvalue.pdf
This paper by Sepandar Kamvar and Taher Haveliwala proves analytically the second eigenvalue of the Google Matrix, which has implications for the PageRank algorithm.

Topic-Sensitive PageRank - http://www2002.org/CDROM/refereed/127/
Taher H. Haveliwala's paper for the 11th International World Wide Web Conference explains that Google proposes to make PageRank reflect importance with respect to a particular topic.

United States Patent: 6,526,440 - http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=/netahtml/search-bool.html&r=1&f=G&l=50&co1=AND&d=ptxt&s1=6,526,440&OS=6,526,440&RS=6,526,440
Ranking search results by reranking the results based on local inter-connectivity. Inventor Krishna Bharat; assignee Google.

WWW2003: Detecting Near-replicas on the Web by Content and Hyperlink Analysis - http://www2003.org/cdrom/papers/poster/p193/p193-diiorio-IE/p193-diiorio.html
Paper by Ernesto Di Iorio, et. al. proposing a technique for finding lists of similar documents, based on a pair of signatures which take into account both the document contents and the hyperlink structure.

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