A scalable adaptive strategy for influence maximization in temporal social networks via vulture based meta heuristic

Over the past decade, social networks have become vital forums for engagement, opinion formation, and information dissemination in areas such as marketing, policymaking, and public health. Identifying key individuals within these networks poses a considerable challenge, especially due to their dynamic nature and broad extent. This article introduces the Adaptive Dynamic Vulture Algorithm (ADVA) as a novel Meta-Heuristic method for improving influence in dynamic social networks. This methodology achieves an optimal balance between exploration and exploitation by prioritizing adaptation to temporal variations in networks and scalability, two aspects often neglected in previous studies. ADVA maintains its efficiency by adaptively adjusting the search methodology in response to changes in network design, such as edge density and node connectivity. The main challenge of this strategy is the computational complexity resulting from the handling of dynamic data. While pruning and indexing approaches alleviate this problem to a degree, they nonetheless result in longer execution times compared to certain alternative solutions. Evaluations on benchmark datasets, such as Stack Overflow and Wiki Talk, demonstrate that ADVA improves penetration by 15% on Stack Overflow and 20% on Wiki Talk compared to prior techniques, while maintaining scalability in large networks. This advantage is attributed to its adaptive techniques and multi-stage optimization; nonetheless, the extended execution time (e.g., 4800 s for a seed size of 60 on Stack Overflow) indicates a need for improvements in computing efficiency.