Reverse Influence Maximization for the Competitive Market in Dynamic Social Networks

Social networks are growing faster, and there is a massive number of active users every day. Therefore, people are using social networks as a potential platform for marketing. Influence Maximization (IM) is such an approach to identify influential users for viral marketing in social networks. Most of the IM algorithms deal with viral marketing profit, which is the maximum number of nodes that can be activated by initial seed nodes. On the other hand, the minimum number of nodes that are required to activated initial seed nodes is called viral marketing cost or seeding cost, which is not focused in most of the existing studies. Therefore, in this paper, we propose a Reverse Influence Maximization (RIM)-based model for seeding cost optimization under the Competitive market in Dynamic (CD-RIM) social networks. The experimental results show that the proposed model outperforms existing RIM models.

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