Novel competitive information propagation macro mathematical model in online social network

Abstract This paper proposes a novel competitive information dissemination macro model CISIR (Competitive Information Susceptible Infected Recovered) in online social network (OSN). Firstly, the Markov chain theory is used to analyze the intrinsic relationship between the competition mechanism among different types of information on the network and the rules of node state transition and information propagation evolution. The macro probability model of node state transition is constructed from the perspective of probability, and the macro information diffusion of network system is constructed from the perspective of statistics. Secondly, the equilibrium point and stability of the proposed model are solved to ensure that the model is reasonable and meaningful. Finally, the relationship between the parameters in the model is analyzed by numerical simulation experiment, and the dynamic simulation is carried out. The process of information competition and dissemination is analyzed, and the degree of agreement between the simulation results and the real event statistics is researched through empirical comparative experiments. Experimental results show that the proposed CISIR model is reasonable and effective. It provides a new scientific method and research approach to solve the problem of competitive propagation of different information types in OSN, and it has high theoretical value and application value.

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