Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
You, Q., Wu, O., Luo, G., Hu, W. (2016). A Probabilistic Matrix Factorization Method for Link Sign Prediction in Social Networks. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-41920-6_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41919-0
Online ISBN: 978-3-319-41920-6
eBook Packages: Computer ScienceComputer Science (R0)