Fighting webspam: detecting spam on the graph via content and link features

Authors:
Yu-Jiu Yang;Shuang-Hong Yang;Bao-Gang Hu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences and Beijing Graduate School, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences and Beijing Graduate School, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences and Beijing Graduate School, Chinese Academy of Sciences, Beijing, China
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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Abstract

We address a novel semi-supervised learning strategy for Web Spam issue. The proposed approach explores graph construction which is the key of representing data semantical relationship, and emphasizes on label propagation from multi views under consistency criterion. Furthermore, we infer labels for the rest of the unlabeled nodes in fusing spectral space. Experiments on the Webspam Challenging dataset validate the efficiency and effectiveness of the proposed method.