  We propose a new Graph Neural Network that combines recent advancements in
the field. We give theoretical contributions by proving that the model is
strictly more general than the Graph Isomorphism Network and the Gated Graph
Neural Network, as it can approximate the same functions and deal with
arbitrary edge values. Then, we show how a single node information can flow
through the graph unchanged.
