Transductive link spam detection

ABSTRACT

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 built on link-based web ranking methods.

In contrast, we cast the link spam detection problem into a machine learning problem of classification on directed graphs. We develop discrete analysis on directed graphs, and construct a discrete analogue of classical regularization theory via discrete analysis. A classification algorithm for directed graphs is then derived from the discrete regularization. We have applied the approach to real-world link spam detection problems, and encouraging results have been obtained.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

Additional Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Performance, Theory

Keywords:
directed graph, discrete analysis, discrete regularization, link spam, transductive inference