Prediction of New Media Information Dissemination Speed and Scale Effect Based on Large-Scale Graph Neural Network

Abstract

In recent years, because of the popularity of the internet and mobile devices, the dissemination of new media in social networks has attracted extensive attention from scholars and the industry. Scale prediction or propagation speed prediction is to use the initial data to predict the propagation scale of the network. In the complex and changeable social network, how to accurately predict the cascading scale of new media information is the biggest problem at present. In the process of new media information transmission, because of the role of new media information transmission in guiding public opinion, the current hierarchical model of new media information transmission lacks the overall and local models. To solve this problem, a global structure modeling method is proposed. In addition, because of the uncertainty of new media information dissemination, a method of bidirectional recurrent neural network prediction and algorithm complexity is used, and a new method based on large-scale graph neural network is constructed. A prediction method of new media information dissemination speed and scale based on large-scale graph neural network. Through comparative experiments with previous research models, it is found that the NWIDF model constructed in this paper has a good prediction effect.