Un-biasing the Link Farm Effect in PageRank Computation

Abstract

Link analysis is a critical component of current Internet search engines' results ranking software, which determines the ordering of query results returned to the user. The ordering of query results can have an enormous impact on web traffic and the resulting business activity of an enterprise; hence businesses have a strong interest in having their Web pages highly ranked in search engine results. This has led to attempts to artificially inflate page ranks by spamming the link structure of the Web. Building an artificial condensed link structure called a "link farm" is one technique to influence a page ranking system, such as the popular PageRank algorithm. In this paper, we present an approach to remove the bias due to link farms from PageRank computation. We propose a method to first measure the PageRank weight accumulated by link farms, and then distribute the weight to other web pages by a modification of the transition matrix in the standard PageRank algorithm. We present results of a selected Web graph that is manually spammed. The results show that the proposed approach can effectively reduce the bias from link farms in PageRank computation.