A Link Prediction Method Based on Neural Networks

被引:4
作者
Li, Keping [1 ]
Gu, Shuang [1 ]
Yan, Dongyang [1 ]
机构
[1] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 11期
基金
北京市自然科学基金; 中国国家自然科学基金;
关键词
link prediction; global network structure reliability; neural network; network evolution; network structure optimization; COMPLEX NETWORKS; RELIABILITY; SECURITY; SYSTEMS; GRAPH;
D O I
10.3390/app11115186
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.
引用
收藏
页数:19
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