  By leveraging recent progress of stochastic gradient descent methods, several
works have shown that graphs could be efficiently laid out through the
optimization of a tailored objective function. In the meantime, Deep Learning
(DL) techniques achieved great performances in many applications. We
demonstrate that it is possible to use DL techniques to learn a graph-to-layout
sequence of operations thanks to a graph-related objective function. In this
paper, we present a novel graph drawing framework called (DNN)^2: Deep Neural
Network for DrawiNg Networks. Our method uses Graph Convolution Networks to
learn a model. Learning is achieved by optimizing a graph topology related loss
function that evaluates (DNN)^2 generated layouts during training. Once
trained, the (DNN)^ model is able to quickly lay any input graph out. We
experiment (DNN)^2 and statistically compare it to optimization-based and
regular graph layout algorithms. The results show that (DNN)^2 performs well
and are encouraging as the Deep Learning approach to Graph Drawing is novel and
many leads for future works are identified.
