LEVERAGING PERSISTENCE AND GRAPH NEURAL NETWORKS FOR ENHANCED INFORMATION POPULARITY FORECASTING

Accurately forecasting the popularity of online information is critical for optimizing content delivery, recommendation systems, and network resource allocation. This paper introduces a novel framework that leverages temporal persistence patterns and graph neural networks (GNNs) to improve the prediction of information popularity. By modeling user-content interactions as dynamic graphs and incorporating historical popularity trends, our approach captures both structural and temporal dependencies. Extensive experiments on real-world social and content-sharing platforms demonstrate that the proposed method significantly outperforms traditional forecasting models in terms of accuracy and robustness. The results highlight the potential of combining graph-based learning with temporal analysis for intelligent information propagation modeling.