  We introduce a novel variant of GNN for particle tracking called Hierarchical
Graph Neural Network (HGNN). The architecture creates a set of higher-level
representations which correspond to tracks and assigns spacepoints to these
tracks, allowing disconnected spacepoints to be assigned to the same track, as
well as multiple tracks to share the same spacepoint. We propose a novel
learnable pooling algorithm called GMPool to generate these higher-level
representations called "super-nodes", as well as a new loss function designed
for tracking problems and HGNN specifically. On a standard tracking problem, we
show that, compared with previous ML-based tracking algorithms, the HGNN has
better tracking efficiency performance, better robustness against inefficient
input graphs, and better convergence compared with traditional GNNs.
