The Large Hadron Collider (LHC) collides millions of protons per second, yielding a rich, multi-dimensional dataset with unique mathematical constraints. The raw data from particle detector electronics readouts must be processed to identify the interactions, trajectories, and decays of individual particles in order to enable downstream physics measurements. Traditional approaches to these tasks have relied on constructing physics-motivated variables from the raw data and using these variables as input to physics-based fits and algorithms. Recently, however, recent work has demonstrated that geometric deep learning approaches can effectively leverage the inherent geometries and relationships in raw collider data, often resulting in more efficient and more accurate particle reconstruction. This talk will describe the use of a range of recent GNN architectures for physics reconstruction tasks and, in particular, will focus on the use edge-classifying GCNs, Interaction Networks, and 3D Instance Segmentation techniques for the task of charged particle trajectory reconstruction, or tracking.