Applying meta-heuristic dynamic algorithms to maximize impact and discover significant nodes in social networks

Social media platforms have transformed global communication, shaping domains such as marketing, public health, and politics by enabling mass information dissemination. Identifying influential individuals in these networks termed the influence maximization problem is critical for optimizing information diffusion strategies like product marketing and disease prevention. Traditional algorithms often struggle with the dynamic and scalable nature of modern social networks, rendering them inadequate for real-world applications. To address these challenges, this study introduces the Advanced Dynamic Generalized Vulture Algorithm (ADGVA), a meta-heuristic approach designed to handle network dynamism effectively. ADGVA combines dynamic exploration–exploitation mechanisms, hierarchical analysis, and predictive modeling to identify high-influence nodes with enhanced accuracy while reducing computational complexity. Unlike traditional methods, it dynamically adapts to evolving network structures through an iterative seed set adjustment mechanism, maintaining high performance even in large-scale networks. The algorithm’s efficiency and robustness are validated through experiments on real-world datasets, demonstrating superior scalability, precision, and influence spread compared to state-of-the-art approaches. The study also highlights potential limitations, including sensitivity to parameter settings and computational cost in highly dynamic environments, laying the groundwork for future research. By bridging computational feasibility with practical applicability, ADGVA sets a new benchmark for influence maximization, offering transformative solutions for the analysis and optimization of dynamic social networks.