PageRank optimization applied to spam detection
This work addresses spam detection in web search ranking, offering an incremental improvement over existing methods like TrustRank and AntiTrustRank.
The authors tackled the problem of link spam detection by introducing MaxRank, a new algorithm that modifies PageRank with a cost function penalizing spam pages and hyperlink removals, and demonstrated its superior performance over TrustRank and AntiTrustRank on the WEBSPAM-UK2007 dataset.
We give a new link spam detection and PageRank demotion algorithm called MaxRank. Like TrustRank and AntiTrustRank, it starts with a seed of hand-picked trusted and spam pages. We define the MaxRank of a page as the frequency of visit of this page by a random surfer minimizing an average cost per time unit. On a given page, the random surfer selects a set of hyperlinks and clicks with uniform probability on any of these hyperlinks. The cost function penalizes spam pages and hyperlink removals. The goal is to determine a hyperlink deletion policy that minimizes this score. The MaxRank is interpreted as a modified PageRank vector, used to sort web pages instead of the usual PageRank vector. The bias vector of this ergodic control problem, which is unique up to an additive constant, is a measure of the "spamicity" of each page, used to detect spam pages. We give a scalable algorithm for MaxRank computation that allowed us to perform experimental results on the WEBSPAM-UK2007 dataset. We show that our algorithm outperforms both TrustRank and AntiTrustRank for spam and nonspam page detection.