Cost minimizing heuristic based seed marketing in social networks

Nowadays, the best way to reach a massive number of individuals within a short period is through social networks, and therefore, people are using social networks for Viral Marketing (VM) or Seed Marketing (SM). Influence Maximization (IM) is a social network service for estimating a set of influential seed users who maximize viral influence or profit. The profit is defined as the maximum number of nodes that can be activated by seed users after they are initially activated. On the other hand, the minimum number of nodes needed to activate all the seed users is called the Seed Marketing Cost (SMC), and the process is not addressed by most existing studies. Moreover, some Reverse Influence Maximization (RIM) models are available to estimate the SM cost; however, the models are incapable of handling the RIM challenges properly. Therefore, in the paper, we propose a Cost-minimization Heuristic (CmH) model, which not only minimizes the SM cost but also resolves the RIM challenges efficiently. We introduce the Reverse Independent Cascade (rIC) model by modifying the traditional Independent Cascade (IC) technique to estimate the SM cost. Further, we also contribute a greedy cost-minimizing heuristic approach to optimize the SM cost. Moreover, employing the influence decay concept ensures a better convergence of the proposed model. The experimental results of the proposed CmH model on real and synthesized datasets shows that it outperforms the existing RIM models.