Social network link prediction method based on subgraph evolution and improved ant colony optimization algorithm

被引：0

作者：

Gu Q. ^{[1
,2
,3
]}

Ju C. ^{[4
]}

Wu G. ^{[4
]}

机构：

[1] School of Management, Zhejiang University of Technology, Hangzhou

[2] China Institute for Small and Medium Enterprises, Zhejiang University of Technology, Hangzhou

[3] Business School, University of Nottingham Ningbo, Ningbo

[4] School of Management Science & Engineering, Zhejiang Gongshang University, Hangzhou

来源：

Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Ant colony optimization algorithm; Link prediction; Social network; Subgraph evolution;

D O I：

10.11959/j.issn.1000-436x.2020223

中图分类号：

学科分类号：

摘要：

Based on improved ant colony algorithm and subgraph evolution fusion, a new unsupervised social network link prediction method (SE-ACO) was proposed. First, the special subgraph was determined in the social network graph. Then the evolution of the subgraph was studied to predict the new links in the graph, and the special subgraph was located by the ant colony method. Finally, using different network topology environments and data sets to test the proposed method. Compared with other unsupervised social network prediction algorithms, the proposed SE-ACO method has the best evaluation results, shorter running time and the best effect on most data sets, which indicates that graph structure plays an important role in link prediction algorithm. © 2020, Editorial Board of Journal on Communications. All right reserved.

引用

页码：21 / 35

页数：14

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