Targeted Influence Maximization Using Centrality and Diminishing Weight

Social Networks are an integral part of our daily life. These networks are tools for sharing information among different users. In such a network, the most influential users become the source for disseminating information over the network. Hence, social network analysis was done to understand and identify the importance of a node in information diffusion. These influential nodes help to achieve maximum influence throughout the network. However, in certain cases, only a subset of nodes are needed to be influenced. This is called targeted influence maximization. In this paper, we use different centrality measures and diminishing weights to identify influential nodes and study the influence of these nodes on the target nodes. Our simulation shows that the seed selection scheme using centrality and diminishing weights activates more target nodes in a network than the existing schemes.