diffusion models

Social networks have become the primary platforms for information dissemination, significantly influencing user interactions and shaping information trends. Understanding how information spreads in these networks is crucial not only for optimizing recommendation algorithms and digital marketing strategies but also for identifying fake news and analyzing user influence. This paper provides a comprehensive review of information diffusion models, analyzing the strengths and weaknesses of traditional approaches such as probabilistic, deterministic, and influence-based models. Subsequently, a Hybrid Deep Learning Model (DLHM) is proposed, integrating Graph Neural Networks (GNNs) for modeling user relationships and Reinforcement Learning (RL) for optimizing the diffusion process. This combination enables the model to learn complex network structures while dynamically selecting optimal strategies for maximizing information spread. Experimental results indicate that the proposed model improves prediction accuracy by 25% compared to classical models and performs better in terms of scalability in large-scale networks. These findings demonstrate that combining deep learning with classical models can significantly enhance the analysis and prediction of information diffusion in social networks.