Modularity-Driven Influence: An Enhanced LPA Framework for Community Detection and Maximization in Social Networks

It is quite an uphill task to identify social network communities as well as to diffuse impact because of their complex architecture as well as evolving nature of their relationships. In this paper, we present an advanced version of the Modularity-Driven Label Propagation Algorithm, Enhanced Label Propagation Algorithm (ELPA) which has provided solutions to these issues through the adoption of modularity optimization and the local influence effectiveness measure. The modularity component ensures accurate and coherent community discovery, while the Local Influence Effectiveness (LIE) metric optimizes effective node selection and network impact propagation. The productivity and the impact distribution of the method have surpassed the results of the Louvain method which is often used on large databases of networks. The findings suggest that this new LPA is of a linearly increasing runtime with better impact diffusion making it appropriate for the use in viral marketing, public health activities and the dissemination of specific information. By integrating computational scale with analytical precision, the methodology advanced in this study offers a robust and efficient strategy for the current evolution of social networks research.