I introduce a graphical representation for modeling multiagent systems based on different kinds of reasoning about agent behavior. I seek to investigate this graphical model's predictive and representative capabilities across various domains, and examine methods for learning the graphical structure from agent interaction data. I also propose to explore the framework's scalability in large real-world scenarios, such as social networks, and evaluate its prediction performance with existing network behavior models.