Abstract
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.
Highlights
A network is a special case to express the relationship between systems
By conducting two kinds of experiments on several networks with N = 30, 50, 80 and 100, we prove that the neural network method is the best in improving network efficiency and global network structure reliability compared with different link prediction models
As for the optimized networks produced by neural networks, compared to the optimized network produced by the link prediction models, the number of medium-sized hubs increased
Summary
Network systems have attracted more and more attention from various disciplines in modeling, network topology and structure optimization [1]. We know that these real networks are constantly evolving in actual operation to ensure the sustainable development of various fields. A series of network evolution models and algorithms aiming at optimizing the structure are emerged as the time requires [2]. As an essential mechanism for network evolution, link prediction has received widespread attention as soon as it was proposed, and it has been practically applied in many fields [3]. Network evolution aims to extend the network topology to ensure the long-term development and regular operation of networks. It is necessary to study link prediction with the purpose of improving reliability in network evolution
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