  Graph Drawing techniques have been developed in the last few years with the
purpose of producing aesthetically pleasing node-link layouts. Recently, the
employment of differentiable loss functions has paved the road to the massive
usage of Gradient Descent and related optimization algorithms. In this paper,
we propose a novel framework for the development of Graph Neural Drawers (GND),
machines that rely on neural computation for constructing efficient and complex
maps. GNDs are Graph Neural Networks (GNNs) whose learning process can be
driven by any provided loss function, such as the ones commonly employed in
Graph Drawing. Moreover, we prove that this mechanism can be guided by loss
functions computed by means of Feedforward Neural Networks, on the basis of
supervision hints that express beauty properties, like the minimization of
crossing edges. In this context, we show that GNNs can nicely be enriched by
positional features to deal also with unlabelled vertexes. We provide a
proof-of-concept by constructing a loss function for the edge-crossing and
provide quantitative and qualitative comparisons among different GNN models
working under the proposed framework.
