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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Yang, Y. J., Yang, S. H., & Hu, B. G. (2008). Fighting WebSpam: Detecting spam on the graph via content and link features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5012 LNAI, pp. 1049–1055). https://doi.org/10.1007/978-3-540-68125-0_112
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