Deep Neural Network for DrawiNg Networks, (DNN) ²

International audienceBy 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) 2 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