# A Link Prediction Method Based on Neural Networks
## 1 Introduction  
A network is a special case to express the relationship between systems. In recent years, network systems have attracted more and more attention from various disciplines in modeling, network topology and structure optimization [1]. Generally, network system is often seen in reliability engineering practice and real life, such as communication networks, computer networks, circuit networks, traffic networks, urban water supply networks, power supply networks, etc. However, we know that these real networks are constantly evolving in actual operation to ensure the sustainable development of various fields. Therefore, 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]. At present, the research on link prediction mainly focuses on evaluating the accuracy and precision of missing links. For many real networks, network evolution aims to extend the network topology to ensure the long-term development and regular operation of networks. Therefore, it is necessary to study link prediction with the purpose of improving reliability in network evolution.  
However, the structure complexity and diversity of real networks bring great difficulties to link prediction aiming at improving reliability. In recent years, various disciplines have devoted themselves to intelligent research in related fields. Artificial intelligence has been at the forefront of the development of science and technology around the world with its advantages of improving productivity and reducing the difficulty of work. It has exerted a profound influence on all fields and promoted the evolution of society. We have to say that artificial intelligence of networks is the general trend. Among them, the neural network has become the representative of artificial intelligence methods because of the powerful self-learning function and the ability to find optimal solutions. It provides strong support for link prediction based on improving reliability.  
Therefore, in order to ensure the safe operation and sustainable development of networks, an artificial intelligence method based on neural networks is used to realize link prediction in order to improve reliability in network evolution. The main contributions of this paper are as follows.  
1. We try to combine artificial intelligence method with complex network theory, and propose a link prediction method based on neural networks.
2. A link pruning strategy based on the greedy algorithm is applied to solve the problem of neural network generalization on experiments.
3. According to the traditional reliability, the quantitative formula of global network structure reliability is given to measure the performance of extended networks.
4. 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.  
The structure of the remainder of the paper is as follows. The work related to link prediction and reliability is reviewed in Section 2. Section 3 introduces the research base, including traditional link prediction models and the evaluation indexes. In Section 4, the neural network algorithm is described. In Section 5, we present the experimental results and conclusions are given in Section 6.