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  Authoritative Sources in a Hyperlinked Environment http://www.cs.cornell.edu/home/kleinber/auth.pdf
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 of possibly relevant pages). Ranking is performed at query time.
  Topic -Sensitive Page Rank http://www2002.org/CDROM/refereed/127/
Integrates ODP data in PageRank calculation for performing query time probabilistic ranking.
  The Clever Project http://www.almaden.ibm.com/cs/k53/clever.html
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.
  Survey on Google's PageRank http://pr.efactory.de/
Information on the algorithm, how to increase PageRank, what diminishes it and how to distribute PageRank within a website.
  Web-Trec 8 and PageRank http://trec.nist.gov/pubs/trec8/papers/acsys.pdf
About the using of PageRank in Web Track 8 "large" and "small" datasets.
  PageRank Computation Methods http://www2002.org/CDROM/poster/173.pdf
A poster paper by Stanford db group which describes iterative methods for calculating PageRank.
  PageRank: A Circuital Analysis http://www2002.org/CDROM/poster/165.pdf
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.
  A Significant Improvement to Clever Algorithm in Hyperlinked Environment http://www2002.org/CDROM/poster/171.pdf
Paper by Minhua Wang describing an improvement to Kleimberg's algorithm.
  Improvement of HITS-based Algorithms on Web Documents http://www2002.org/CDROM/refereed/643/
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.
  Web Page Scoring Systems for Horizontal and Vertical Search http://www2002.org/CDROM/refereed/629/
"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.
  Larry Page Describes PageRank http://www-db.stanford.edu/~backrub/pageranksub.ps
Postscript-format slides which introduces citation importance ranking by Larry Page, Google's founder.
  The PageRank Citation Ranking: Bringing Order to the Web http://ilpubs.stanford.edu:8090/422/
First Stanford paper about PageRank. It is a static ranking, performed at indexing time, which interprets a link from page A to page B as a vote, by page A, for page B. Web is seen as a direct graph and votes recursively propagate from nodes to nodes. Ranking is performed at indexing time. Used by Google.
  The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank http://www.cs.washington.edu/homes/pedrod/papers/nips01b.pdf
This method uses query dependent importance scores and a probabilistic approach to improve upon PageRank. It pre-computes importance scores offline for every possible text query.
  SALSA: The Stochastic Approach for Link-Structure Analysis http://www.cs.technion.ac.il/~moran/r/PS/lm-feb01.ps
A focused search algorithm (SALSA) based on Markov chains. It starts with a query on a broad topic, discards useless links, and then weights the remaining terms. A stochastic crawl is used to discover the authorities on this topic. [PS format]
  The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity http://www.cs.cmu.edu/~cohn/papers/nips00.pdf
This paper describes a joint probabilistic model for modeling the contents and inter-connectivity of document collections such as sets of web pages or research paper archives.
  Extrapolation Methods for Accelerating PageRank Computations http://www.stanford.edu/~sdkamvar/papers/extrapolation.pdf
A paper about the computation of PageRank using the standard Power Method and the new Quadratic Extrapolation which computes the principal eigenvector of the Markov matrix representing the Web link graph with an increased speed up of about 50-300%.
  The EigenTrust Algorithm for Reputation Management in P2P Networks http://www.stanford.edu/~sdkamvar/papers/eigentrust.pdf
An eingenvalues algorithm for calculating reputation in P2P networks and isolating malicious peers. There is a relationship with PageRank algorithm.
  DiscoWeb: Discovering Web Communities Via Link Analysis http://www.research.rutgers.edu/~davison/discoweb/
This paper describes a prototype system, later known as the Teoma Search Engine. It performs a Link Analysis, loosely based on the Kleimberg method, and computed at query time.
  Adaptive On-Line Page Importance Computation http://www2003.org/cdrom/papers/refereed/p007/p7-abiteboul.html
A good explanation about the convergence of various algorithms. This paper also describes an adaptive and on-line algorithm for computing the page importance. It can be used for focus crawling as well as for search engine's ranking.
  What is this Page Known for? Computing Web Page Reputations, http://www.cs.ualberta.ca/~drafiei/papers/www9.ps
PageRank and Hub and Authority generalization based on the topic of Web Pages. Definition of a model where a surfer can move forward (following an out-going link) and backward (following an in-going link in the inverse direction). [PS format]
  Web-Trec 9 and Link Popularity http://trec.nist.gov/pubs/trec9/papers/unine9.pdf
About the using of Link Popularity in Web Track 9 datasets.
  WWW2003 - Scaling Personalized Web Search http://www2003.org/cdrom/papers/refereed/p185/html/p185-jeh.html
Presentation paper. Link Popularity algorithms biased according to a user-specified set of given interesting pages.
  PageRank U.S. Patent 6,285,999 http://patft.uspto.gov/netacgi/nph-Parser?patentnumber=6285999
Lawrence Page's PageRank Patent.
  Finding Authorities and Hubs From Link Structures on the World Wide Web http://www10.org/cdrom/papers/314/
A survey on PageRank, Hits and SALSA. It also describes two Bayesian statistical algorithms for ranking of hyperlinked documents and the concepts of monotonicity and locality, as well as various concepts of distance and similarity between ranking algorithms.
  Improved Algorithms for Topic Distillation in Hyperlinked Environments http://gatekeeper.dec.com/pub/DEC/SRC/publications/monika/sigir98.pdf
Given a typical user query to find quality documents related to the query topic. It uses an Hits variation.
  The World’s Largest Matrix Computation http://www.mathworks.com/company/newsletters/news_notes/clevescorner/oct02_cleve.html
"Google's PageRank is an eigenvector of a matrix of order 2.7 billion"
  PageRank Calculation with Lossy Encoding http://www-db.stanford.edu/~taherh/papers/encoding-pagerank.pdf
Lossy encoding for large scale PageRank calculation.

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