  A game process is a system where the decisions of one agent can influence the
decisions of other agents. In the real world, social influences and
relationships between agents may influence the decision makings of agents with
game behaviors. And in turn, this also gives us the opportunity to mine such
information from agents by the observed interactions of them in a game process.
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 generate networks for game processes, which opens a door for understanding
agents with game behaviors by network mining approaches. We illustrate how to
apply GGNs to infer the hidden relationships between agents with game behaviors
and conduct experiments on team games as a concrete example. Experimental
results demonstrate that our proposed framework can reveal the hidden
relationships of agents in such games.
