  Learning from graph-structured data is an important task in machine learning
and artificial intelligence, for which Graph Neural Networks (GNNs) have shown
great promise. Motivated by recent advances in geometric representation
learning, we propose a novel GNN architecture for learning representations on
Riemannian manifolds with differentiable exponential and logarithmic maps. We
develop a scalable algorithm for modeling the structural properties of graphs,
comparing Euclidean and hyperbolic geometry. In our experiments, we show that
hyperbolic GNNs can lead to substantial improvements on various benchmark
datasets.
