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Trust Rank

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Submitted By tianlan
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Combating Web Spam with TrustRank
Zolt´ n Gy¨ ngyi a o
Stanford University Computer Science Department Stanford, CA 94305 zoltan@cs.stanford.edu

Hector Garcia-Molina
Stanford University Computer Science Department Stanford, CA 94305 hector@cs.stanford.edu

Jan Pedersen
Yahoo! Inc. 701 First Avenue Sunnyvale, CA 94089 jpederse@yahoo-inc.com

Abstract
Web spam pages use various techniques to achieve higher-than-deserved rankings in a search engine’s results. While human experts can identify spam, it is too expensive to manually evaluate a large number of pages. Instead, we propose techniques to semi-automatically separate reputable, good pages from spam. We first select a small set of seed pages to be evaluated by an expert. Once we manually identify the reputable seed pages, we use the link structure of the web to discover other pages that are likely to be good. In this paper we discuss possible ways to implement the seed selection and the discovery of good pages. We present results of experiments run on the World Wide Web indexed by AltaVista and evaluate the performance of our techniques. Our results show that we can effectively filter out spam from a significant fraction of the web, based on a good seed set of less than 200 sites.

creation of a large number of bogus web pages, all pointing to a single target page. Since many search engines take into account the number of incoming links in ranking pages, the rank of the target page is likely to increase, and appear earlier in query result sets. Just as with email spam, determining if a page or group of pages is spam is subjective. For instance, consider a cluster of web sites that link to each other’s pages repeatedly. These links may represent useful relationships between the sites, or they may have been created with the express intention of boosting the rank of each other’s pages. In general, it is

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