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Abstract
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The rapid advancements in complex network theory and its applications have spurred the development of numerous methods aimed at enhancing the efficiency of influence spread, such as in rumor containment and viral marketing. However, traditional approaches often overlook the critical role of community structures in shaping diffusion processes. Communities provide a natural segmentation of networks that can be exploited to improve both the scalability and accuracy of influence maximization strategies. By targeting influential nodes within these communities, it is possible to accelerate the diffusion process while reducing computational complexity. This research seeks to fill this gap by integrating community-based insights, synergistic gravity dynamics, and novel H-Index centrality into influence maximization algorithms, thus providing a more robust and realistic framework for real-world applications.
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