Graph-Driven Deep Learning Framework for Predictive Analytics in Dynamic Social Networks

Abstract

Dynamic social networks are characterized by continuously evolving structures, complex relational dependencies, and large-scale heterogeneous interactions, posing significant challenges for accurate predictive analytics. Traditional machine learning approaches often fail to effectively capture temporal dynamics and high-order graph relationships inherent in such networks. To address these limitations, this paper proposes a Graph-Driven Deep Learning Framework that integrates graph representation learning with temporal deep neural architectures for predictive analytics in dynamic social networks. The proposed framework leverages Graph Neural Networks (GNNs) to model structural dependencies among nodes while employing temporal learning mechanisms, such as recurrent or attention-based models, to capture the evolution of interactions over time. By jointly learning spatial-temporal features from dynamic graph data, the framework enables accurate prediction of network behaviors, including user influence propagation, link formation, community evolution, and information diffusion. Extensive experimental evaluations conducted on benchmark dynamic social network datasets demonstrate that the proposed approach consistently outperforms state-of-the-art baseline models in terms of prediction accuracy, robustness, and scalability. The results highlight the effectiveness of graph-driven deep learning in uncovering complex patterns within evolving social systems, making the framework suitable for real-world applications such as recommendation systems, social media analytics, and online behavioral prediction.