基于社交网络图节点度的神经网络个性化传播算法研究

Graph is an important and fundamental data structure that presents in a wide variety of practical scenarios. With the rapid development of the Internet in recent years, there has been a huge increase in social network graph data, and the analysis of this data can be of great help in practical scenarios such as public services and advertising and marketing. There are already quite a few graph neural network algorithms that can get good results in such problems, but they still have room for improvement, and in many scenarios where high accuracy is pursued, engineers still want to have algorithms with better performance to choose from. This paper improves personalized propagation of neural predictions and proposes a new graph neural network algorithm called degree of node based personalized propagation of neural predictions (DPPNP) that can be used in social graph networks. Compared to traditional graph neural network algorithms, when the information is propagated between nodes, the proposed algorithm will keep its own information for different nodes in different proportions according to the degree of nodes, so as to improve the accuracy. Experiments on real datasets show that the proposed algorithm has better performance compared to previous graph neural network algorithms.