  We introduce a novel architecture for graph networks which is equivariant to
any transformation in the coordinate embeddings that preserves the distance
between neighbouring nodes. In particular, it is equivariant to the Euclidean
and conformal orthogonal groups in $n$-dimensions. Thanks to its equivariance
properties, the proposed model is extremely more data efficient with respect to
classical graph architectures and also intrinsically equipped with a better
inductive bias. We show that, learning on a minimal amount of data, the
architecture we propose can perfectly generalise to unseen data in a synthetic
problem, while much more training data are required from a standard model to
reach comparable performance.
