Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2014, Vol. 37 ›› Issue (5): 80-84.doi: 10.13190/j.jbupt.2014.05.017

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Integrating Multi-Feature for Link Sign Prediction in Signed Networks

ZHANG Wei-yu1,2, WU Bin1, LIU Yang1   

  1. 1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. School of Information, Qilu University of Technology, Jinan 250353, China
  • Received:2014-01-20 Online:2014-10-28 Published:2014-11-07

Abstract:

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.

Key words: signed networks, link sign prediction, social network analysis, structure balance theory

CLC Number: