A Heuristic Mixed Model for Viral Marketing Cost Minimization in Social Networks
The Influence maximization (IM) aims at estimating a small number of influential users that maximize the viral marketing profit whereas, the Reverse Influence Maximization (RIM) deals with the minimization of Viral Marketing (VM) cost in social networks. Here, the VM cost is measured by the minimum number of nodes that are required to activate seed nodes and the profit is defined by the maximum number of nodes influenced by seed users when they are initially activated. However, most of the existing works focus on the profit maximization without considering the VM cost. Thus, in this research, we introduce a Viral Marketing Cost (VMC) Minimization problem and propose a Heuristic Mixed (HM) approach under mixed Reverse Independent Cascade (RIC) and Reverse Linear Threshold (RLT) diffusion models. The proposed HM model employs the greedy technique along with a heuristic approach to optimize the VM cost. Moreover, our model resolves the challenging issues of the RIM problem more efficiently. Finally, we simulate our model using data sets of two real social networks, and the result shows that our model outperforms the baseline and existing models.
2019 International Conference on Information Networking (ICOIN)