The explosive growth and the widespread accessibility of the Web has led to a surge of research activity in the area of information retrieval on the World Wide Web. The seminal papers of Kleinberg [1998, 1999] and Brin and Page [1998] introducedLink Analysis Ranking, where hyperlink structures are used to determine the relativeauthorityof a Web page and produce improved algorithms for the ranking of Web search results. In this article we work within the hubs and authorities framework defined by Kleinberg and we propose new families of algorithms. Two of the algorithms we propose use a Bayesian approach, as opposed to the usual algebraic and graph theoretic approaches. We also introduce a theoretical framework for the study of Link Analysis Ranking algorithms. The framework allows for the definition of specific properties of Link Analysis Ranking algorithms, as well as for comparing different algorithms. We study the properties of the algorithms that we define, and we provide an axiomatic characterization of the INDEGREE heuristic which ranks each node according to the number of incoming links. We conclude the article with an extensive experimental evaluation. We study the quality of the algorithms, and we examine how different structures in the graphs affect their performance.

A new technique for improving the efficiency of propositional reasoning procedures is presented. The meta-search procedure, ND, is parameterized by a search procedure P and a real number for controlling the way in which P is applied to the given problem. Experiments using SATO on the domain of Crossword Puzzle Construction (CPC) illustrate the potential for ND. Variations of and future experiments with ND are discussed.

Recently, ther have been a number of algorH#-9 prC osed for analyzing hyperDCC link str5#TDso as todeterCDthe best "authorODHUM for a given topic or quer . While such analysis is usually combined with content analysis, ther is a sense in which some algorUO-9 ar deemed to be"mor balanced" andother "mor focused". WeunderDe a compar --C e study of hyperDTD link analysisalgor-CDCO Guided by some experD#C tal quer5CD we prD ose somefor-C crC5O r for evaluating and comparTlink analysisalgor-CUH5 Keywords link analysis, websear hing, hubs, author-9TD5 SALSA, KleinberMU algor9T#5 thrCHHE-9 Bayesian 1.