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A Parameterized Approach to Spam-Resilient Link Analysis of the Web

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4 Author(s)
Caverlee, J. ; Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA ; Webb, S. ; Ling Liu ; Rouse, William B.

Link-based analysis of the Web provides the basis for many important applications-like Web search, Web-based data mining, and Web page categorization-that bring order to the massive amount of distributed Web content. Due to the overwhelming reliance on these important applications, there is a rise in efforts to manipulate (or spam) the link structure of the Web. In this manuscript, we present a parameterized framework for link analysis of the Web that promotes spam resilience through a source-centric view of the Web. We provide a rigorous study of the set of critical parameters that can impact source-centric link analysis and propose the novel notion of influence throttling for countering the influence of link-based manipulation. Through formal analysis and a large-scale experimental study, we show how different parameter settings may impact the time complexity, stability, and spam resilience of Web link analysis. Concretely, we find that the source-centric model supports more effective and robust rankings in comparison with existing Web algorithms such as PageRank.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:20 ,  Issue: 10 )

Date of Publication:

Oct. 2009

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