Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/app11115186
Keywords
link prediction; global network structure reliability; neural network; network evolution; network structure optimization
Categories
Funding
- Beijing Natural Science Foundation [8202039]
- National Natural Science Foundation of China [71942006, 71621001]
- Research Foundation of State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University [RCS2021ZT001]
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This paper introduces a link prediction method based on neural networks, which generates optimized networks with better network efficiency and global network structure reliability compared to traditional methods.
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.
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