  In this draft paper, we introduce a novel architecture for graph networks
which is equivariant to the Euclidean group in $n$-dimensions. The model is
designed to work with graph networks in their general form and can be shown to
include particular variants as special cases. Thanks to its equivariance
properties, we expect the proposed model to be more data efficient with respect
to classical graph architectures and also intrinsically equipped with a better
inductive bias. We defer investigating this matter to future work.
