A Probabilistic Matrix Factorization Method for Link Sign Prediction in Social Networks

  • Qiang YouEmail author
  • Ou Wu
  • Guan Luo
  • Weiming Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)


In this paper, we consider the link sign prediction in social networks with friend and foe relationships. We view the sign prediction as a user-to-user recommendation problem with trust or distrust information. Not only do we take the topological relationships such as the social structural balance and status theories into consideration, but also the social factors that whether a user is trustworthy and whether the user easily trust others are involved. We propose a probabilistic matrix factorization method with social trust and distrust ensembles and the structural theories from social psychology in order to predict link signs in social networks. The experimental results show that our proposed method outperforms those of the previous studies on this problem.


Link sign prediction Matrix factorization Social psychology Signed networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370 (2010)Google Scholar
  2. 2.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 641–650 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CAS Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations