Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction
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 neighbours 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 behaviour of a user but also to predict when he will perform the behaviour. Both the global influence and social influence are explored in the time dependent 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.
Reference IEEE paper:
“Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction”, IEEE Transactions on Knowledge and Data Engineering, 2017.
Unique ID -SBI1041
Domain – DATA MINING