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    1.The PageRank Citation Ranking: Bringing Order to the Web
     By Page, Lawrence; Brin, Sergey; Motwani, Rajeev; Winograd, Terry. Available in Postscript, PDF, and plain text formats.
    dbpubs.stanford.edu:8090
    2.Dynamic Data Mining: Exploring Large Rule Spaces by Sampling
     By Brin, Sergey; Page, Lawrence. Available in Postscript, PDF, and plain text formats.
    dbpubs.stanford.edu:8090
    3.Efficient Crawling Through URL Ordering
     By Cho, Junghoo; Garcia-Molina, Hector; Page, Lawrence. Available in Postscript, PDF, and plain text formats.
    dbpubs.stanford.edu:8090
    4.Extracting Patterns and Relations from the World Wide Web
     By Brin, Sergey. Available in Postscript, PDF, and plain text formats.
    dbpubs.stanford.edu:8090
    5.Finding Near-replicas of Documents on the Web
     By Shivakumar, N.; Garcia-Molina, H. Available in Postscript, PDF, and plain text formats.
    dbpubs.stanford.edu:8090
    6.The Anatomy of a Large-Scale Hypertextual Web Search Engine
     By Brin, Sergey; Page, Lawrence. Available in Postscript, PDF, and plain text formats.
    dbpubs.stanford.edu:8090
    7.Efficient Computation of PageRank
     By Haveliwala, T. Available in Postscript, PDF, and plain text formats.
    dbpubs.stanford.edu:8090
    8.Topic-Sensitive PageRank
     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.
    dbpubs.stanford.edu:8090
    9.A Case Study in Web Search using TREC Algorithms
     Paper from WWW10 by Google employees Amit Singhal and Marcin Kaszkiel.
    www10.org
    10.United States Patent: 6,526,440
     Ranking search results by reranking the results based on local inter-connectivity. Inventor Krishna Bharat; assignee Google.
    patft.uspto.gov
    11.Extrapolation Methods for Accelerating PageRank Computations
     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. [PDF format]
    www.stanford.edu
    12.Exploiting the Block Structure of the Web for Computing PageRank
     This paper by Sepandar Kamvar, Taher Haveliwala, Chris Manning, and Gene Golub presents an algorithm to vastly speed up the computation of PageRank. [PDF format].
    www.stanford.edu
    13.The Second Eigenvalue of the Google Matrix
     This paper by Sepandar Kamvar and Taher Haveliwala proves analytically the second eigenvalue of the Google Matrix, which has implications for the PageRank algorithm. [PDF format].
    www.stanford.edu
    14.Adaptive Methods for the Computation of PageRank
     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. [PDF format].
    www.stanford.edu
    15.The Google File System
     By Ghemawat, Sanjay; Gobioff, Howard; and Leung, Shun-Tak. [PDF]
    www.cs.rochester.edu
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