  We propose a hierarchical graph neural network (GNN) model that learns how to
cluster a set of images into an unknown number of identities using a training
set of images annotated with labels belonging to a disjoint set of identities.
Our hierarchical GNN uses a novel approach to merge connected components
predicted at each level of the hierarchy to form a new graph at the next level.
Unlike fully unsupervised hierarchical clustering, the choice of grouping and
complexity criteria stems naturally from supervision in the training set. The
resulting method, Hi-LANDER, achieves an average of 54% improvement in F-score
and 8% increase in Normalized Mutual Information (NMI) relative to current
GNN-based clustering algorithms. Additionally, state-of-the-art GNN-based
methods rely on separate models to predict linkage probabilities and node
densities as intermediate steps of the clustering process. In contrast, our
unified framework achieves a seven-fold decrease in computational cost. We
release our training and inference code at
https://github.com/dmlc/dgl/tree/master/examples/pytorch/hilander.
