Model of Clustering in interaction diffusion information in social network: an evolutionary game-theoretic perspective

In the real world, diffusion of some contagions at the same time on social network is very important. Due to the large number of contagions that have been diffused on a social network at the same time, in order to reduce the model parameters and also being scalable, the contagions have been grouped and clustered and also all the clusters have been set equal on some of the researches, While this assumption is not acceptable and far from the facts of the real world. To eliminate and fix this issue in this thesis, we try to elaborate the different categories of contagions and consider, analyze and study the whole story as an evolutionary game theory and will calculate its evolution dynamics of contagions and evolutionarily stable strategy stable strategy. Evolutionary dynamics and evolutionarily stable of the contagions show the impact of a contagion in the process of diffusion from the point of view of users in two consecutive interval timing and whether it is promoted or suppressed.