| Directory > Computers > Software > Information Retrieval > Ranking > By Context A Survey of Eigenvector Methods of Web Information Retrieval
A Survey presenting HITS, PageRank and Salza. Enphasis is on eigencvectors and eigenvalues
analysis.
http://www4.ncsu.edu:8030/~anlangvi/HITSPRsurvey.ps
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The EigenTrust Algorithm for Reputation Management in P2P Networks
An eingenvalues algorithm for calculating reputation in P2P networks and isolating malicious peers.
There is a relationship with PageRank algorithm.
http://www.stanford.edu/~sdkamvar/papers/eigentrust.pdf
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Adaptive On-Line Page Importance Computation
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.
http://www2003.org/cdrom/papers/refereed/p007/p7-abiteboul.html
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Extrapolation Methods for Accelerating PageRank Computations
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%.
http://dbpubs.stanford.edu:8090/pub/2003-16
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The Second Eigenvalue of the Google Matrix
A mathematical paper about the convergence of methods used for solving the PageRank Matrix.
http://www.stanford.edu/~taherh/papers/secondeigenvalue.pdf
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Exploiting the Block Structure of the Web for Computing PageRank
A hierarchical approach for computing PageRank. The local PageRanks of page for each host are
computed independently and then used to compute the global PageRank of Web Graph.
http://www.stanford.edu/~taherh/papers/blockrank.pdf
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The PageRank Citation Ranking: Bringing Order to the Web
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.
http://dbpubs.stanford.edu:8090/pub/1999-66
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DiscoWeb: Discovering Web Communities Via Link Analysis
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.
http://www.cs.rutgers.edu/~davison/discoweb/
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Link Analysis in Web Information Retrieval
Survey of query independent and query dependent connectivity based ranking [PS Format]
http://www.research.microsoft.com/research/db/debull/A00sept/henzinge.ps
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PageRank U.S. Patent 6,285,999
Lawrence Page's PageRank Patent.
http://patft.uspto.gov/netacgi/nph-Parser?patentnumber=6285999
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Finding Authorities and Hubs From Link Structures on the World Wide Web
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.
http://www10.org/cdrom/papers/314/
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Link Analysis: Hubs and Authorities on the World Wide Web
A survey on Kleimberg's Hits. [PS format]
http://www.nersc.gov/research/SCG/cding/papers_ps/hits3.ps
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The World's Largest Matrix Computation
A concise description of PageRank seen from a mathematical point of view.
http://www.mathworks.com/company/newsletter/clevescorner/oct02_cleve.shtml
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Survey on Google's PageRank
Information on the algorithm, how to increase PageRank, what diminishes it and how to distribute
PageRank within a website.
http://pr.efactory.de/
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Topic -Sensitive Page Rank
Integrates ODP data in PageRank calculation for performing query time probabilistic ranking.
http://dbpubs.stanford.edu:8090/pub/2002-6
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PageRank Calculation with Lossy Encoding
Lossy encoding for large scale PageRank calculation.
http://dbpubs.stanford.edu:8090/pub/2002-58
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PageRank Calculation Techniques
Describes efficient techniques for computing PageRank.
http://dbpubs.stanford.edu:8090/pub/1999-31
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PageRank Explained
Some useful tips and tricks about Google's PageRank and other by content ranks. [PDF format]
http://212.25.163.79/pagerank/documents/googlerank02.pdf
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Web-Trec 9 and Link Popularity
About the using of Link Popularity in Web Track 9 datasets. [PDF format]
http://trec.nist.gov/pubs/trec9/papers/unine9.pdf
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Web-Trec 8 and PageRank
About the using of PageRank in Web Track 8 "large" and "small" datasets. [PDF
format]
http://trec.nist.gov/pubs/trec8/papers/acsys.pdf
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What Can You Do with a Web in Your Pocket?
One of the first description of Stanford's WebBase and Google's PageRank. [PS format]
http://www.research.microsoft.com/research/db/debull/98june/webbase.ps
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Towards Exploiting Link Evolution
It describes how to compute incrementally PageRank when Web graph's link topology changes. [PS
format]
http://www.almaden.ibm.com/cs/people/siva/papers/linkevol.ps
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Scaling Personalized Web Search
Link Popularity algorithms biased according to a user-specified set of given interesting pages.
http://dbpubs.stanford.edu:8090/pub/2002-12
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The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity
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. [PDF format]
http://www.cs.cmu.edu/~cohn/papers/nips00.pdf
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Probabilistic Combination of Content and Links
It introduces a probabilistic model that integrates link topology (used to identify important
pages), anchor text (used to augment the text of cited pages), and activation (spread to linked
pages). Experiments are on MSN Directory. [PDF format]
http://research.microsoft.com/copyright/accept.asp?path=http://research.microsoft.com/~sdumais/....
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PageRank, HITS and a Unified Framework for Link Analysis
It generalizes and combines PageRank and Hits into a unified framework. [PS format]
http://www.nersc.gov/research/SCG/cding/papers_ps/sigpage6b.ps
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Improvement of HITS-based Algorithms on Web Documents
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.
http://www2002.org/CDROM/refereed/643/
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PageRank: A Circuital Analysis
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. [PDF format]
http://www2002.org/CDROM/poster/165.pdf
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Improvement to Clever Algorithm
A Kleimberg's algorithm improvement. [PDF format]
http://www2002.org/CDROM/poster/171.pdf
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Improved Algorithms for Topic Distillation in Hyperlinked Environments
Given a typical user query to find quality documents related to the query topic. It uses an Hits
variation. [PS format]
http://www.henzinger.com/monika/mpapers/sigir98_ps.ps
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Link Analysis, Eigenvectors, and Stability
Do Hits and PageRank (and some variations) give stable rankings under small perturbations to the
linkage patterns? [PS format]
http://www.cs.berkeley.edu/~alicez/ijcai01-linkanalysis.ps
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Web Page Scoring Systems for Horizontal and Vertical Search
"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.
http://www2002.org/CDROM/refereed/629/
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The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank
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. [PDF format]
http://www.cs.washington.edu/homes/pedrod/papers/nips01b.pdf
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SALSA: The Stochastic Approach for Link-Structure Analysis
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]
http://www.cs.technion.ac.il/~moran/r/PS/lm-feb01.ps
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The Clever Project
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.
http://www.almaden.ibm.com/cs/k53/clever.html
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Authoritative Sources in a Hyperlinked Environment
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. [PDF format]
http://www.cs.cornell.edu/home/kleinber/auth.pdf
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PageRank Used to Characterize Web Structure
PageRank's values on the Web follow a power law. An high in-degree of a node does not imply high
PageRank, and vice versa. [PDF format]
http://www.cs.purdue.edu/homes/gopal/prank.pdf
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What is this Page Known for? Computing Web Page Reputations,
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]
http://www.cs.ualberta.ca/~drafiei/papers/www9.ps
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PageRank Uncovered
A complete and updated analysis of PageRank with several calculation examples. [PDF format]
http://www.supportforums.org/PageRank.pdf
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PageRank Computation Methods
A poster paper by Stanford db group which describes iterative methods for calculating PageRank.
[PDF format]
http://www2002.org/CDROM/poster/173.pdf
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PageRank as a Random Walk
A general framework for measuring the quality of an index and providing the background on the
PageRank and Random Walks. Imagine a Web surfer who wanders the Web. At each step, he/she either
jumps to a page on the Web chosen uniformly at random, or follows a link chosen from those on the
current page.
http://www8.org/w8-papers/2c-search-discover/measuring/measuring.html
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Larry Page Describes PageRank
Slides which introduces citation importance ranking by Larry Page, Google's founder.
http://hci.stanford.edu/~page/papers/pagerank/
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