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Résumé : The rapid growth of social networks has led to the generation of massive, dynamic, and interconnected datasets, necessitating efficient computational frameworks for real-time analysis. Parallel dynamic graph processing has emerged as a critical tool for addressing these challenges, enabling scalable and efficient analysis of evolving social networks. This paper explores the applications of parallel dynamic graph processing in social network analysis, focusing on its role in community detection, influence maximization, anomaly detection, and real-time recommendation systems. By leveraging parallel computing architectures, dynamic graph algorithms can process large-scale, continuously changing network data with reduced latency and improved accuracy. We discuss key techniques, such as incremental computation, load balancing, and distributed memory management, that enhance the performance of dynamic graph processing in social network contexts. Furthermore, we highlight case studies and emerging trends that demonstrate the transformative potential of parallel dynamic graph processing in extracting actionable insights from complex social network data. This work underscores the importance of advancing parallel dynamic graph processing techniques to meet the demands of modern social network analysis.