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.

A poster paper by Stanford db group which describes iterative methods for calculating PageRank. [PDF format]

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]

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]

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]

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.

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]

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]

"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.

Do Hits and PageRank (and some variations) give stable rankings under small perturbations to the linkage patterns? [PS format]

A Kleimberg's algorithm improvement. [PDF format]

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]

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.

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]

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]

It describes how to compute incrementally PageRank when Web graph's link topology changes. [PS format]

About the using of PageRank in Web Track 8 "large" and "small" datasets. [PDF format]

About the using of Link Popularity in Web Track 9 datasets. [PDF format]

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.

Lawrence Page's PageRank Patent.

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.

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.

A mathematical paper about the convergence of methods used for solving the PageRank Matrix.

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.

An eingenvalues algorithm for calculating reputation in P2P networks and isolating malicious peers. There is a relationship with PageRank algorithm.

Postscript-format slides which introduces citation importance ranking by Larry Page, Google's founder.

Given a typical user query to find quality documents related to the query topic. It uses an Hits variation.

"Google's PageRank is an eigenvector of a matrix of order 2.7 billion"

Lossy encoding for large scale PageRank calculation.

Presentation paper. Link Popularity algorithms biased according to a user-specified set of given interesting pages.