Graph Neural Networks (GNNs) have established themselves as the state of the art of encoding the nodes of a graph into a low-dimensional space by extracting features from the connectivity structure of the graph as well as the features of the nodes. However, since the embedding of a node is updated according to the information aggregated from the immediate neighborhood, a GNN tends to capture the community memberships of the nodes better than the other side of the coin: the role memberships, which quantify how much nodes carry out specific functions from a structural point of view. In this paper, we present RC-GNNs, a category of GNNs designed to learn embeddings from the community and the role memberships as well as the features of the nodes. RC-GNNs learn from different versions of the same graph, in which the nodes are connected according to either the community or the role memberships. Results show that, compared with models such as k-hop GNNs, k-GNNs, and MixHop, RC-GNNs are up to 4% more accurate in classifying the nodes of CiteSeer, Cora, and PubMed and up to 3% in classifying the graphs of MUTAG, PROTEINS, and Synthie.