Optimization for Short Video Propagation Based on User Interaction Analysis in Edge Networks

Video is a dominant traffic type in mobile networks. D2D communication, one of the most promising technologies for 6G, enables efficient localized distribution of content and thus reduces latency and bandwidth consumption for content delivery. Discovering potential mobile user devices and caching promotional content or short video content in D2D self-organizing communication-based social networks requires overcoming challenges such as heterogeneous structural features of user interactions and user state changes. We propose a graph neural network based GSVIA framework that integrates user information from the perspective of user interaction behavior and social features, and also propose an InfTMC algorithm for maximizing the weights of a set of caching nodes using aggregate function submodularity. Experimental results under two different weighted IC models show that the InfTMC algorithm improves the influence spread by an average of 46.80% and 45.44% compared to the existing algorithms.