(untitled) (International Journal of Innovative Research in Engineering & Management, 2022)
line social networks (OSNs) have dominated modern life on a global scale. The immense popularity of online social networks increases day by day as they help us in modelling various types of processes like viral marketing, rumor controlling, collaborative filtering, market prediction and controlling diseases spread. In the realm of complex networks research, influence maximisation in social networks has long been a challenging task. Influence maximisation is the method of identifying k-seed nodes or influential nodes in order to increase overall influence in a network. Ranking the nodes using network node-centrality metrics is one of the conventional techniques for finding prominent nodes in a social network. However, estimating global centrality metrics like betweenness centrality is computationally exhaustive and typically not scalable for very large size networks such as a country’s whole population. In this paper, we provide a novel approach for extracting communities from the underlying social network to identify prominent nodes aka "influential nodes”. Experimental results indicate that the seed nodes identified by the proposed approach have high betweenness centrality in the social network thus rendering the proposed approach significant.