  Spherical data is found in many applications. By modeling the discretized
sphere as a graph, we can accommodate non-uniformly distributed, partial, and
changing samplings. Moreover, graph convolutions are computationally more
efficient than spherical convolutions. As equivariance is desired to exploit
rotational symmetries, we discuss how to approach rotation equivariance using
the graph neural network introduced in Defferrard et al. (2016). Experiments
show good performance on rotation-invariant learning problems. Code and
examples are available at https://github.com/SwissDataScienceCenter/DeepSphere
