ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (4): 764-775.doi: 10.7544/issn1000-1239.2016.20151079

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Exploring Interactional Opinions and Status Theory for Predicting Links in Signed Network

Wang Xin1,3,4, Wang Ying2,3, Zuo Wanli2,3   

  1. 1School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012); 2College of Computer Science and Technology, Jilin University, Changchun 130012); 3Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012); 4Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), Guilin, Guangxi 541004)
  • Online:2016-04-01

Abstract: With the wide spread and pervasion of social network, it brings more opportunities and novel problems for deep research on signed network, where link prediction is one of key problems in signed network. Interactional opinions and status theory are contributed to explain the construction and sign property of link relations, and provide theoretical principles for improving prediction quality. Therefore, this paper investigates link prediction problem in signed network from the perspective of interactional opinions and status theory, and constructs link prediction model by studying the strong correlation between two inducements and link relationship. Firstly, it explores interactional opinions to enhance the reliability of the decomposed matrix, and makes up for the limitations of status theory. Then, it models interactional opinions as enhanced reliability factor of matrix, and models status theory as the regularization terms. Finally, we construct the model of link prediction in signed network, namely MF-SI. Experimental results demonstrate that the model of MF-SI owns the best prediction quality compared with other baseline methods, which shows that the method of integrating interactional opinions with status theory implements link prediction in signed network.

Key words: signed network, link prediction, sparse learning, interactional opinions, status theory

CLC Number: