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PageRank optimization applied to spam detection

Olivier Fercoq 1, 2 
1 MAXPLUS - Max-plus algebras and mathematics of decision
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
2 ERGO - Edinburgh Research Group in Optimization
School of Mathematics - Operational Research and Optimization
Abstract : 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.
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Submitted on : Wednesday, January 30, 2013 - 4:44:06 PM
Last modification on : Thursday, January 20, 2022 - 5:28:52 PM


  • HAL Id : hal-00782844, version 1



Olivier Fercoq. PageRank optimization applied to spam detection. 6th International conference on NETwork Games, COntrol and OPtimization (Netgcoop), Nov 2012, Avignon, France, France. ⟨hal-00782844⟩



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