Research on Media Communication Based on Graph Neural Network

With the continuous development of social media and digital platforms, media communication research is facing new challenges and opportunities. As a powerful deep learning method, graph neural network provides a new perspective and tool for media communication research. This paper, titled “Research on Media Communication Based on Graph Neural Networks, aims to explore the application of graph neural networks in the field of media communication and its impact on communication mode and user behavior. This paper first reviews the history and traditional methods of media communication research, and points out the problems and limitations faced in the era of social media. Then, the basic principles and characteristics of graph neural networks are introduced, including graph structure representation, graph convolutional neural network (GCN) and graph attention network (GAT). Through graph neural network, researchers can better capture the user relationship, information transmission path and propagation effect in media communication, so as to realize the modeling and analysis of complex network environment.