Identification of Influential Nodes Based on Overlapping Communities Structure to Maximize Influence in Social Networks

Maximizing influence propagation in modern social networks is essential for applications like viral marketing, information diffusion, and network resilience. Traditional methods, like centrality-based metrics and community-based algorithms often miss the overlapping community patterns that restrict their effectiveness in information diffusion in real-world networks. This paper proposes an alternative approach to maximize influence spread by using overlapping communities and combining several centrality metrics including eigenvector, betweenness, and closeness centralities with sophisticated overlapping community detection techniques, like the Clique-Based Louvain Algorithm (CBLA). This makes it possible to determine important nodes that serve as links inside and outside communities, facilitating the spread of influence. Applied to real-world datasets, this method enhances influence spread, improves community quality, and addresses overlapping influence. The proposed approach provides a robust solution to computational challenges in identifying influential nodes, making it well-suited for complex social networks.