Influence Maximization in Social Network using Community Detection and Node Modularity

Research in the area of identifying the most influential users in social networks is currently regarded to be one of the most important areas of study. Through the examination of the most influential users on social networks, it is possible to analyze and, in some cases, manage the dissemination of information. A technique that is both quick and scalable is proposed in this research as a means of identifying the users with maximum diffusion capabilities in online social networks. This approach is suited for directed networks as well as undirected networks. The approach that has been suggested is comprised of four stages: (1) community detection, which involves the partial partitioning of the whole social network into communities that are connected to one another by the use of the Louvain algorithm; (2) the removal of communities that are not suitable; (3) selection of prominent nodes within the particular community; and (4) selection of the top k seed nodes. Experimental research was carried out on a number of datasets, each of which was of a different complexity. Using imperfect social networks, it has been demonstrated that the findings generate better outcomes for the diffusion of influence than the current related work models, and they do so with a much less amount of processing time being required.