Harmonic functions based semi-supervised learning for web spam detection

Authors:
Weifeng Zhang;Danmei Zhu;Yinzhou Zhang;Guoqiang Zhou;Baowen Xu
Affiliations:
Nanjing University of Posts and Telecommunications, Nanjing, China;Nanjing University of Posts and Telecommunications, Nanjing, China;Nanjing University of Posts and Telecommunications, Nanjing, China;Nanjing University of Posts and Telecommunications, Nanjing, China;Nanjing University, Nanjing, China
Venue:
Proceedings of the 2011 ACM Symposium on Applied Computing
Year:
2011

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Abstract

In web spam detection, we propose a new semi-supervised learning algorithm named HFSSL (harmonic functions based semi-supervised learning). In our method, labeled and unlabeled web pages are represented as vertices in a weighted graph. The learning problem is then modeled as a Gaussian random field on this graph, where the mean of the field is characterized by harmonic functions, which can be efficiently obtained using matrix methods. The experiments on standard WEBSPAM-UK2006 show that our algorithm is effective.