The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authoritative ” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages ” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.

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The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects." This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.

When using traditional search engines, users have to formulate queries to describe their information need. This paper discusses a different approach toweb searching where the input to the search process is not a set of query terms, but instead is the URL of a page, and the output is a set of related web pages. A related web page is one that addresses the same topic as the original page. For example, www.washingtonpost.com is a page related to www.nytimes.com, since both are online newspapers. We describe two algorithms to identify related web pages. These algorithms use only the connectivity information in the web (i.e., the links between pages) and not the content of pages or usage information. We haveimplemented both algorithms and measured their runtime performance. To evaluate the e ectiveness of our algorithms, we performed a user study comparing our algorithms with Netscape's \What's Related " service [12]. Our study showed that the precision at 10 for our two algorithms are 73 % better and 51 % better than that of Netscape, despite the fact that Netscape uses both content and usage pattern information in addition to connectivity information.

The combination of different text representations and search strategies has become a standard technique for improving the effectiveness of information retrieval. Combination, for example, has been studied extensively in the TREC evaluations and is the basis of the “meta-search” engines used on the Web. This paper examines the development of this technique, including both experimental results and the retrieval models that have been proposed as formal frameworks for combination. We show that combining approaches for information retrieval can be modeled as combining the outputs of multiple classifiers based on one or more representations, and that this simple model can provide explanations for many of the experimental results. We also show that this view of combination is very similar to the inference net model, and that a new approach to retrieval based on language models supports combination and can be integrated with the inference net model.

In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical figures vary, overall trends cited

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.

: Clustering separates unrelated documents and groups related documents, and is useful for discrimination, disambiguation, summarization, organization, and navigation of unstructured collections of hypertext documents. We propose a novel clustering algorithm that clusters hypertext documents using words (contained in the document), out-links (from the document) , and in-links (to the document). The algorithm automatically determines the relative importance of words, out-links, and in-links for a given collection of hypertext documents. We annotate each cluster using six information nuggets: summary, breakthrough, review, keywords, citation, and reference. These nuggets constitute high-quality information resources that are representatives of the content of the clusters, and are extremely effective in compactly summarizing and navigating the collection of hypertext documents. We employ web searching as an application to illustrate our results. Keywords: cluster annotation, feature comb...

Two popular webpage ranking algorithms are HITS and PageRank. HITS emphasizes mutual reinforcement between authority and hub webpages, while PageRank emphasizes hyperlink weight normalization and web surfing based on random walk models. We systematically generalize/combine these concepts into a unified framework. The ranking framework contains a large algorithm space; HITS and PageRank are two extreme ends in this space. We study several normalized ranking algorithms which are intermediate between HITS and PageRank, and obtain closed-form solutions. We show that, to first order approximation, all ranking algorithms in this framework, including PageRank and HITS, lead to same ranking which is highly correlated with ranking by indegree.

We are interested in finding natural communities in largescale linked networks. Our ultimate goal is to track changes over time in such communities. For such temporal tracking, we require a clustering algorithm that is relatively stable under small perturbations of the input data. We have developed an e#cient, scalable agglomerative strategy and applied it to the citation graph of the NEC CiteSeer database (250,000 papers; 4.5 million citations). Agglomerative clustering techniques are known to be unstable on data in which the community structure is not strong. We find that some communities are essentially random and thus unstable while others are natural and will appear in most clusterings. These natural communities will enable us to track the evolution of communities over time.