ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (4): 764-775.doi: 10.7544/issn1000-1239.2016.20151079

• 网络技术 • 上一篇    下一篇

基于交互意见和地位理论的符号网络链接预测模型

王鑫1,3,4,王英2,3,左万利2,3   

  1. 1(长春工程学院计算机技术与工程学院 长春 130012);2(吉林大学计算机科学与技术学院 长春 130012);3(符号计算与知识工程教育部重点实验室(吉林大学) 长春 130012);4(广西可信软件重点实验室(桂林电子科技大学) 广西桂林 541004) (xinwangjlu@gmail.com)
  • 出版日期: 2016-04-01
  • 基金资助: 
    国家自然科学基金项目(61300148);吉林省科技计划青年科研基金项目(20130532112JH);广西可信软件重点实验室研究课题资助(kx201533)

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

摘要: 随着社会网络的普遍和流行,社会网络为符号网络(signed network)的深入研究提出了更多的全新问题,其中链接预测是符号网络研究的核心问题之一.交互意见和地位理论能够较好地解释符号网络中链接关系的构建和链接的符号属性,二者作为链接构建的诱因为提高链接预测的质量提供了理论基础.因此,通过研究交互意见和地位理论与链接关系的强相关性,构建符号网络链接预测模型.首先,利用交互意见增强待分解矩阵的可靠度,弥补由地位理论的对称性所带来的局限性;然后,在稀疏学习矩阵分解模型基础上,将交互意见建模为增强可靠度因子,同时将地位理论建模为稀疏学习模型的正则化项;最后,构建基于交互意见和地位理论的符号网络链接预测模型MF-SI.实验结果表明相比于其他基本方法,MF-SI模型获得了较好的预测质量,说明集成交互意见和地位理论能够较好地实现符号网络链接预测问题.

关键词: 符号网络, 链接预测, 稀疏学习, 交互意见, 地位理论

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

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