Influence Maximization Towards Target Users on Social Networks for Information Diffusion

Influence maximization has been an area of active research in recent years. This study aims to extend the fundamental influence maximization problem (IMP) with respect to a set of target users on a social network. It is important to aim the target users to speed up the rate of information diffusion and reduce the information diffusion cost. In doing so, two enhanced greedy algorithms were formulated and compared with modified heuristic algorithms. The publicly available Wiki-vote dataset was used. It was found that the greedy algorithms reduced the seed set size by 88.6% on the average while identifying all target users. The information diffusion cost function (IDCF) of the greedy algorithms was 79% lower even after identifying all the target nodes. It was equally seen that random influencer selection identifies target nodes better than the betweenness and PageRank centralities. The findings would help organizations to reach target users on social media in the shortest cycle.