The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Information spreading in context
Proceedings of the 20th international conference on World wide web
Content based social behavior prediction: a multi-task learning approach
Proceedings of the 20th ACM international conference on Information and knowledge management
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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 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.