%0 Journal Article
%A LIU Yang
%A WU Bin
%A ZHANG Wei-yu
%T Integrating Multi-Feature for Link Sign Prediction in Signed Networks
%D 2014
%R 10.13190/j.jbupt.2014.05.017
%J Journal of Beijing University of Posts and Telecommunications
%P 80-84
%V 37
%N 5
%X <p>In order to make the link sign prediction more accurate in signed networks, it is necessary to analyse each underlying principle of generating signed networks. Structure balance theory and status theory are extended to gain more information for link sign prediction. A new measurement named PageTrust in web network is introduced to describe the importance of node of signed networks. On the basis of integrating different kind principles of generating signed networks, a group of refined features are extracted. Based on those creative features, two link sign predictors using supervised machine learning algorithms are established. Experimental results on two real signed networks demonstrate that learned model is more accurate and generalized than other state-of-the-art methods.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2014.05.017