Before revision:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.
After revision:  In this paper, we propose a Game Generative Network (GGN) framework that utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.