Dynamic model of information diffusion based on multidimensional complex network space and social game

In social networks, information diffusion is affected by network topology and driving factors. In this work, we investigate the structure of networks, map networks into multidimensional network space, and apply user social behavioral and psychological features to social networks. First, by exploring the network topological structure, we map it into three network spaces: behavior influence, attribute influence, and topological influence subnets. With a hierarchical process, the coupling relationship among the subnets can be reduced, and we can analyze the effect of the driving factors of each subnet on information diffusion separately. Second, assuming that the psychological characteristics of topic users are the driving factors affecting information diffusion, the concept of topic heat is defined. On the basis of evolutionary game theory, a dynamic evolution strategy of user behavior is proposed. In this way, we can explore the effect of a user’s psychological game on information diffusion. Finally, on the basis of the traditional Susceptible–Infected–Recovered (SIR) model, the improved diffusion model is obtained by combining the network topology with the user’s social behavior and psychological characteristics. The effectiveness of the model is verified by its implementation on the Tencent microblog dataset. The experimental results indicate that the model can better describe the trend of information diffusion in social networks.