Link Prediction Based on Clustering Information in Scientific Coauthorship Networks

被引：1
|
作者：
Ma, Yang [1 ]
Cheng, Guangquan [1 ]
Liu, Zhong [1 ]
Liang, Xingxing [1 ]
机构：
[1] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China
来源：
2016 IEEE FIRST INTERNATIONAL CONFERENCE ON DATA SCIENCE IN CYBERSPACE (DSC 2016) | 2016年
基金：
中国国家自然科学基金; 国家教育部博士点专项基金资助;
关键词：
spectral clustering; link prediction; scientific coauthorship;
D O I：
10.1109/DSC.2016.58
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Link prediction in social networks has become a growing concern among researchers. In this paper, clustering index (CI) is calculated to predict potential links with characteristics of scientific cooperation network taken into consideration. Compared with traditional universal algorithms, algorithm for specific type of network can better reflect the features of the network and achieve better predicting results. Experiments show that CI performs better than traditional indices in scientific coauthorship networks.
引用
收藏
页码：668 / 672
页数：5
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