Recovering parameter values from mathematical models is a primary interest of those who use them to model the physical and biological world. This recovery, or identification, of parameters within models is also an interesting mathematical problem that we call Identifiability. In this talk, we will explore the identifiability of a specific type of model called Linear Compartmental Tree Models, which are often used to understand biological phenomena and have an underlying graphical structure. Specifically, we will explore the relationship between this graphical structure of Linear Compartmental Models and the corresponding model identifiability.