@techreport{ilprints697,
          number = {2005-33},
           month = {October},
          author = {Zoltan Gyongyi and Pavel Berkhin and Hector Garcia-Molina and Jan Pedersen},
           title = {Link Spam Detection Based on Mass Estimation},
            type = {Technical Report},
       publisher = {Stanford},
            year = {2005},
     institution = {Stanford InfoLab},
         journal = {Technical Report},
        keywords = {web search; link spam detection},
             url = {http://ilpubs.stanford.edu:8090/697/},
        abstract = {Link spamming intends to mislead search engines and trigger an artificially high link-based ranking of specific target web pages. This paper introduces the concept of spam mass, a measure of the impact of link spamming on a page's ranking. We discuss how to estimate spam mass and how the estimates can help identifying pages that benefit significantly from link spamming. In our experiments on the host-level Yahoo! web graph we use spam mass estimates to successfully identify tens of thousands of instances of heavy-weight link spamming.}
}