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• W2997889485 abstract "The Reverse Influence Maximization (RIM) model deals with the viral marketing cost minimization in social networks. On the other hand, the Influence maximization (IM) technique finds the small number of influential users that maximize the viral marketing profit. Here, the profit is defined by the maximum number of nodes that can be influenced by seed users when they are initially activated. On the other hand, the cost is measured by the minimum number of nodes required to activate all the seed nodes. However, most of the existing studies focus on profit maximization without considering the seeding cost. Moreover, the most profitable node may not always be a cost-effective one. Thus, in this research, we introduce an epidemic Cost Minimization model under the Competitive Market in Dynamic social networks (CMCMD, i.e., CM2D). In the CM2D model, we modify the Susceptible-Infected-Recovered (SIR) technique as the Reverse SIR (RSIR) model to employ in the node activation process. The proposed CM2D model also uses the greedy Set Cover approximation technique to optimize the seeding cost. Moreover, the CM2D model tackles the challenging issues of the RIM problem more efficiently than the existing models. Finally, we simulate our model using datasets of two synthesized and four real datasets, and the results show that the CM2D model outperforms the existing models." @default.
• W2997889485 created "2020-01-10" @default.
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• W2997889485 date "2020-01-01" @default.
• W2997889485 modified "2023-10-18" @default.
• W2997889485 title "CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks" @default.
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• W2997889485 doi "https://doi.org/10.1109/access.2019.2963324" @default.
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