Dynamic Self-Attention Network based opinion formation over dynamic social networks with application to live-streaming

Opinion Dynamics (OD) is a widely used framework for studying the evolution of group opinions in complex social networks. However, most existing models primarily focus on static networks or small-scale dynamic networks, leaving large-scale dynamic networks largely underexplored. To address this gap, this paper investigates the evolution of group opinions and behavior prediction in large-scale dynamic social networks, using live-streaming data as a case study. Specifically, we propose two novel models: (1) a Dynamic Community Network (DCN) model, which constructs dynamic networks based on real-world big data, and (2) the Real-Time Dynamic Self-Attention Network Hegselmann–Krause (RT-DySAT-HK) model, which integrates Dynamic Graph Neural Networks (DGNNs) with OD to model the evolution of group opinions and predict behaviors. Through empirical analysis and simulations, we demonstrate that user behaviors in dynamic live-streaming networks are significantly influenced by community stability. Notably, during the early and middle stages of live-streaming, community size plays a critical role in attracting and retaining users. Moreover, the RT-DySAT-HK model proves highly effective in real-time group behavior prediction, particularly in large-scale dynamic networks. Compared to baseline models, it excels in extracting high-quality node representations and achieving accurate behavior predictions. Additionally, our findings reveal that the evolution of group opinions is influenced by multiple factors, including the contradictory effects of opinion weights and update speeds, which can lead to opinion polarization. Excessively slow update speeds may also result in opinion fragmentation. These insights contribute to a deeper understanding of OD in large-scale, dynamic environments and offer practical implications for predicting and managing group behaviors in real-world scenarios.