| Publisher | Association for Computing Machinery | ||
|---|---|---|---|
| Format | 313.6KB PDF | Date added | 08 May 2007 |
| Topics | Network Security, Spam - E-mail Fraud - Phishing | ||
| Downloads | 3 | ||
Web spam can significantly deteriorate the quality of search engines. Early web spamming techniques mainly manipulate page content. Since linkage information is widely used in web search, link-based spamming has also developed. So far, many techniques have been proposed to detect link spam. Those approaches are basically variants of link-based web ranking methods. In contrast, the paper cast the link spam detection problem into a machine learning problem of classification on directed graphs. This paper develops discrete analysis on directed graphs, and constructs a discrete analogue of classical regularization theory via discrete analysis. A classification algorithm for directed graphs is then derived from the discrete regularization. This paper has applied the approach to real-world link spam detection problems, and encouraging results have been obtained.
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