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A game process is a system where the decisions of one agent can influence thedecisions of other agents. In the real world, social influences andrelationships between agents may influence the decision makings of agents withgame behaviors. And in turn, this also gives us the opportunity to mine suchinformation from agents by the observed interactions of them in a game process.In this paper, we propose a Game Generative Network (GGN) framework thatutilizes the deviation between the real game outcome and the ideal game modelto generate networks for game processes, which opens a door for understandingagents with game behaviors by network mining approaches. We illustrate how toapply GGNs to infer the hidden relationships between agents with game behaviorsand conduct experiments on team games as a concrete example. Experimentalresults demonstrate that our proposed framework can reveal the hiddenrelationships of agents in such games.

A Game Generative Network Framework with its Application to Relationship Inference