Optimizing Diffusion Time of the Content Through the Social Networks: Stochastic Learning Game

Both customers and companies have a great interest to optimize the diffusion time. The contents generators always try to disseminate their information in the minimum time in order to benefit the most of the received reward. In our paper, we suppose that each node in the social network is interested to diffuse its content with the goal of optimizing its delivery time and selling its information to the receivers. Each content generator must target its adapted neighbors, who will play the role of relay and will allow the arrival of the information to its destination before the expiry of its time. The objective of our work is to disseminate the content through neighbors characterized by a high connectivity and a high quality of relationships in terms of being interested to share the same type of information. We model our problem as a stochastic learning game, where each player tries to maximize its utility function by selecting the optimal action depending on the state of the system and on the action taken by the competitor.

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