We present a novel algorithm for the fast computation of PageRank, a hyperlink-based estimate of the "importance" of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Quadratic Extrapolation, accelerates the convergence of the Power Method by periodically subtracting off estimates of the nonprincipal eigenvectors from the current iterate of the Power Method. In Quadratic Extrapolation, we take advantage of the fact that the first eigenvalueof a Markov matrix is known to be 1 to compute the nonprincipal eigenvectorsusing successiveiterates of the Power Method. Empirically, we show that using Quadratic Extrapolation speeds up PageRank computation by 50-300% on a Web graph of 80 million nodes, with minimal overhead.

Abstract—The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative “importance ” of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. For ordinary keyword search queries, we compute the topicsensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared. By using linear combinations of these (precomputed) biased PageRank vectors to generate context-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. We describe techniques for efficiently implementing a large-scale search system based on the topic-sensitive PageRank scheme. Index Terms—Web search, web graph, link analysis, PageRank, search in context, personalized search, ranking algorithm.

Web sites today serve many different functions, such as corporate sites, search engines, e-stores, and so forth. As sites are created for different purposes, their structure and connectivity characteristics vary. However, this research argues that sites of similar role exhibit similar structural patterns, as the functionality of a site naturally induces a typical hyperlinked structure and typical connectivity patterns to and from the rest of the Web. Thus, the functionality of Web sites is reflected in a set of structural and connectivity-based features that form a typical signature. In this paper, we automatically categorize sites into eight distinct functional classes, and highlight several search-engine related applications that could make immediate use of such technology. We purposely limit our categorization algorithms by tapping connectivity and structural data alone, making no use of any content analysis whatsoever. When applying two classification algorithms to a set of 202 sites of the eight defined functional categories, the algorithms correctly classified between 54.5 % and 59 % of the sites. On some categories, the precision of the classification exceeded 85%. An additional result of this work indicates that the structural signature can be used to detect spam rings and mirror sites, by clustering sites with almost identical signatures.

Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge numbers of features. Most previous studies found that the majority of these features are relevant for classification, and that the performance of text categorization with support vector machines peaks when no feature selection is performed.

Topical crawlers are becoming important tools to support applications such as specialized Web portals, online searching, and competitive intelligence. As the Web mining field matures, the disparate crawling strategies proposed in the literature will have to be evaluated and compared on common tasks through welldefined performance measures. This paper presents a general framework to evaluate topical crawlers. We identify a class of tasks that model crawling applications of di#erent nature and di#culty. We then introduce a set of performance measures for fair comparative evaluations of crawlers along several dimensions including generalized notions of precision, recall, and e#ciency that are appropriate and practical for the Web. The framework relies on independent relevance judgements compiled by human editors and available from public directories. Two sources of evidence are proposed to assess crawled pages, capturing di#erent relevance criteria. Finally we introduce a set of topic characterizations to analyze the variability in crawling e#ectiveness across topics. The proposed evaluation framework synthesizes a number of methodologies in the topical crawlers literature and many lessons learned from several studies conducted by our group. The general framework is described in detail and then illustrated in practice by a case study that evaluates four public crawling algorithms. We found that the proposed framework is e#ective at evaluating, comparing, di#erentiating and interpreting the performance of the four crawlers. For example, we found the IS crawler to be most sensitive to the popularity of topics.

Web spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the sense that the seed set used by TrustRank may not be sufficiently representative to cover well the different topics on the Web. Also, for a given seed set, TrustRank has a bias towards larger communities. We propose the use of topical information to partition the seed set and calculate trust scores for each topic separately to address the above issues. A combination of these trust scores for a page is used to determine its ranking. Experimental results on two large datasets show that our Topical TrustRank has a better performance than TrustRank in demoting spam sites or pages. Compared to TrustRank, our best technique can decrease spam from the top ranked sites by as much as 43.1%.

In this article, we define webometrics within the framework of informetric studies and bibliometrics, as belonging to library and information science, and as associated with cybermetrics as a generic subfield. We develop a consistent and detailed link typology and terminology and make explicit the distinction among different Web node levels when using the proposed conceptual framework. As a consequence, we propose a novel diagram notation to fully appreciate and investigate link structures between Web nodes in webometric analyses. We warn against taking the analogy between citation analyses and link analyses too far.

Although text categorization is a burgeoning area of IR research, readily available test collections in this field are surprisingly scarce. We describe a methodology and system (named Accio) for automatically acquiring labeled datasets for text categorization from the World Wide Web, by capitalizing on the body of knowledge encoded in the structure of existing hierarchical directories such as the Open Directory. We define parameters of categories that make it possible to acquire numerous datasets with desired properties,whichin turn allow better control over categorization experiments. In particular, we develop metrics that estimate the di#culty of a dataset by examining the host directory structure. These metrics are shown to be good predictors of categorization accuracy that can be achieved on a dataset, and serve as e#cient heuristics for generating datasets subject to user's requirements. A large collection of automatically generated datasets are made available for other researchers to use.

Classification of web page content is essential to many tasks in web information retrieval such as maintaining web directories and focused crawling. The uncontrolled nature of web content presents additional challenges to web page classification as compared to traditional text classification, but the interconnected nature of hypertext also provides features that can assist the process. As we review work in web page classification, we note the importance of these web-specific features and algorithms, describe state-of-the-art practices, and track the underlying assumptions behind the use of information from neighboring pages. 1

By supplying different versions of a web page to search engines and to browsers, a content provider attempts to cloak the real content from the view of the search engine. Semantic cloaking refers to differences in meaning between pages which have the effect of deceiving search engine ranking algorithms. In this paper, we propose an automated two-step method to detect semantic cloaking pages based on different copies of the same page downloaded by a web crawler and a web browser. The first step is a filtering step, which generates a candidate list of semantic cloaking pages. In the second step, a classifier is used to detect semantic cloaking pages from the candidates generated by the filtering step. Experiments on manually labeled data sets show that we can generate a classifier with a precision of 93% and a recall of 85%. We apply our approach to links from the dmoz Open Directory Project and estimate that more than 50,000 of these pages employ semantic cloaking.