Ranking By Context
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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]
The PageRank Citation Ranking: Bringing Order to the Web
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.
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.
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.
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. [PDF]
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.
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. [PDF]
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.
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]
Larry Page Describes PageRank - Postscript-format slides which introduces citation importance ranking by Larry Page, Google's founder.
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]
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]
PageRank Calculation with Lossy Encoding - Lossy encoding for large scale PageRank calculation. [PDF]
PageRank Computation Methods - A poster paper by Stanford db group which describes iterative methods for calculating PageRank. [PDF]
PageRank U.S. Patent 6,285,999 - Lawrence Page's PageRank Patent.
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]
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]
A Significant Improvement to Clever Algorithm in Hyperlinked Environment - Paper by Minhua Wang describing an improvement to Kleimberg's algorithm. [PDF]
Survey on Google's PageRank - Information on the algorithm, how to increase PageRank, what diminishes it and how to distribute PageRank within a website.
Topic -Sensitive Page Rank - Integrates ODP data in PageRank calculation for performing query time probabilistic ranking.
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.
Web-Trec 9 and Link Popularity - About the using of Link Popularity in Web Track 9 datasets. [PDF]
Web-Trec 8 and PageRank - About the using of PageRank in Web Track 8 "large" and "small" datasets. [PDF]
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]
The World’s Largest Matrix Computation - "Google's PageRank is an eigenvector of a matrix of order 2.7 billion"
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%. [PDF] (May, 2003)
WWW2003 - Scaling Personalized Web Search - Presentation paper. Link Popularity algorithms biased according to a user-specified set of given interesting pages. [PDF] (May, 2003) Help build the largest human-edited directory on the web.