HSF-Mobile V2 Net based Influence Maximization model for large scale networks

Abstract

Influence Maximization (IM) in online social networks deals with selection of seed nodes to maximize their influence spread underlying any influence diffusion models. In this paper, Heaviside Step Function (HSF)-Mobile V2-based IM model in social networks is proposed. At first, large-scale networks, such as Facebook, Instagram, etc. are established. Then, the graph is constructed for the large-scale network using the Sociogram technique. After that, graph analytics is performed for evaluating the influence score. On the other hand, popular Messages/event's propagation distance is calculated for evaluating the influence score of the most influenced node. The influence score is given as input into the HSF-Mobile V2 Net model for selecting the seed node. For these selected seed nodes, a new graph is constructed. Then, the baseline strength and requests are measured for the nodes presented in the newly constructed graph. Finally, the child node from the constructed graph is considered for evaluating the most influenced user using Naive-GCA algorithm. Lastly, experimentation analysis is done to verify the superiority of the proposed methodology.