ISSN 1000-1239 CN 11-1777/TP

• 综述 •

### 符号社会网络中正负关系预测算法研究综述

1. 1(数据工程与知识工程教育部重点实验室(中国人民大学) 北京 100872); 2(中国人民大学信息学院 北京 100872); 3(山东理工大学计算机学院 山东淄博 255091) (lanmengwei@ruc.edu.cn)
• 出版日期: 2015-02-01
• 基金资助:
基金项目：国家“九七三”重点基础研究发展计划基金项目(2014CB340402,2012CB316205);国家“八六三”高技术研究发展计划基金项目(2014AA015204)；国家自然科学基金项目(61272137,61033010,61202114)；国家社科基金项目(12&ZD220)

### Survey of Sign Prediction Algorithms in Signed Social Networks

Lan Mengwei1,2,Li Cuiping1,2, Wang Shaoqing1,2,3,Zhao Kankan1,2, Lin Zhixia1,2,Zou Benyou1,2, Chen Hong1,2

1. 1(Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872); 2(Information School, Renmin University of China, Beijing 100872); 3(School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255091)
• Online: 2015-02-01

Abstract: According to the potential meaning, the edges in some networks can be divided into positive and negative relationships. When we mark these positive and negative edges with plus and minus signs respectively, a signed network is formed. Signed networks are widespread in sociology, information science, biology and other fields. Nowadays signed networks have become one of research hotspots. Researching on sign prediction problem in signed social networks is valuable to personalized recommendation, abnormal node identification and user clustering in social networks. This paper focus on predicting positive and negative links in signed social networks, and describes domestic and overseas current research status and latest developments. First we introduce the social structural balance theory and status theory. Then we classify several sign prediction algorithms into two categories according to their main ideals: algorithms based on matrix and algorithms based on classification. We introduce the basic idea of these sign prediction algorithms in detail. And then we compare and analyze these algorithms from multiple perspectives such as speed, accuracy, scalability and so on. Finally, we summarize some regularity characteristics and challenges in sign prediction and discuss some possible development directions in signed social networks research.