With the development of computer technology and the enhancement of people's cognition of the world, more and more scholars have been focusing on the research of complex networks. At the same time, the emerging machine learning neural network algorithm has become a powerful tool for various researchers. This paper mainly discusses the construction and clustering of complex networks based on neural network algorithm. Firstly, the development history and main application fields of neural network are introduced. Then, several common methods of complex network clustering are summarized, and then the limitations of these clustering methods are discussed. At last, it proposes to improve the construction of neural network through the concept of small world in complex network and enhance the effect of complex network clustering by the characteristics of neural network algorithm, including the accuracy, reliability, stability, speed, etc.
Extracting Community Structures in Complex Networks Based on Discrete Neural Network Algorithm
