How to model the process of information diffusion in social networks is a critical research task. Although numerous attempts have been made for this study, few of them can simulate and predict the temporal dynamics of the diffusion process. To address this problem, we propose a novel information diffusion model (GT model), which considers the users in network as intelligent agents. The agent jointly considers all his interacting neighbors and calculates the payoffs for his different choices to make strategic decision. We introduce the time factor into the user payoff, enabling the GT model to not only predict the behavior of a user but also to predict when he will perform the behavior. Both the global influence and social influence are explored in the timedependent payoff calculation, where a new social influence representation method is designed to fully capture the temporal dynamic properties of social influence between users. Experimental results on Sina Weibo and Flickr validate the effectiveness of our methods.
Some of the works are focused on develop dynamic predictive models using different methods such as considering the nodes of the network as intelligent agents who make strategic decisions REF .
Modeling information diffusion over social networks for temporal dynamic prediction
CIKM
Computer Science