The Community for Technology Leaders
RSS Icon
Hong Kong
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2701-7
pp: 1049-1053
Guoyang Shen , Microsoft Research Asia, China; Tsinghua University, China
Bin Gao , Microsoft Research Asia, China
Tie-Yan Liu , Microsoft Research Asia, China
Guang Feng , Microsoft Research Asia, China; Tsinghua University, China
Shiji Song , Tsinghua University, China
Hang Li , Microsoft Research Asia, China
How to effectively protect against spam on search ranking results is an important issue for contemporary web search engines. This paper addresses the problem of combating one major type of web spam: ?link spam.? Most of the previous work on anti link spam managed to make use of one snapshot of web data to detect spam, and thus it did not take advantage of the fact that link spam tends to result in drastic changes of links in a short time period. To overcome the shortcoming, this paper proposes using temporal information on links in detection of link spam, as well as other information. Specifically, it defines temporal features such as In-link Growth Rate (IGR) and In-link Death Rate (IDR) in a spam classification model (i.e., SVM). Experimental results on web domain graph data show that link spam can be successfully detected with the proposed method.
Guoyang Shen, Bin Gao, Tie-Yan Liu, Guang Feng, Shiji Song, Hang Li, "Detecting Link Spam Using Temporal Information", ICDM, 2006, Sixth International Conference on Data Mining (ICDM'06), Sixth International Conference on Data Mining (ICDM'06) 2006, pp. 1049-1053, doi:10.1109/ICDM.2006.51
27 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool