Modern particle physics detectors generate copious amounts of data packed with meaning that provides the means for high-quality measurements in demanding experimental environments. To achieve these measurements there is a trend towards finer granularity in these detectors and that implies the data read out has less intrinsic structure. Accurate pattern recognition is required to define the signatures of particles within those detectors and simultaneously extract physical parameters for the particles. Typically, algorithms to achieve these goals are written using well known unsupervised algorithms, but recent advances in machine learning on graph structures, "Graph Neural Networks" (GNNs), provide powerful new methodologies for designing pattern recognition algorithms. In particular, methodologies for predicting the link structure between pieces of data from detectors are well suited to the particle physics pattern recognition task. Furthermore, there are interesting avenues for enforcing known symmetries of the data into the output of such networks and there is ongoing research in this direction. This talk will discuss the challenges of pattern recognition, the advent of GNNs and the connections to particle physics, and the paths of research ahead for fully utilizing this powerful new tool.