Link prediction in social network based on local information and attributes of nodes

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Liang, Yingying^1,2 ; Huang, Lan^1,2,3 ; Wang, Zhe^1,2

College of Computer Science and Technology, Jilin University, Changchun

130012, China^1

Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Ministry of Education, Changchun

130012, China^2

Zhuhai College of Jilin University, Zhuhai

519041, China^3

关键词: Co-authorship networks;    Feature vectors;    Information sources;    Link prediction;    Local information;    Topological information;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012043/pdf
DOI  :  10.1088/1742-6596/887/1/012043

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Link prediction is essential to both research areas and practical applications. In order to make full use of information of the network, we proposed a new method to predict links in the social network. Firstly, we extracted topological information and attributes of nodes in the social network. Secondly, we integrated them into feature vectors. Finally, we used XGB classifier to predict links using feature vectors. Through expanding information source, experiments on a co-authorship network suggest that our method can improve the accuracy of link prediction significantly.