  While a multitude of studies have been conducted on graph drawing, many
existing methods only focus on optimizing a single aesthetic aspect of graph
layouts, which can lead to sub-optimal results. There are a few existing
methods that have attempted to develop a flexible solution for optimizing
different aesthetic aspects measured by different aesthetic criteria.
Furthermore, thanks to the significant advance in deep learning techniques,
several deep learning-based layout methods were proposed recently. These
methods have demonstrated the advantages of deep learning approaches for graph
drawing. However, none of these existing methods can be directly applied to
optimizing non-differentiable criteria without special accommodation. In this
work, we propose a novel Generative Adversarial Network (GAN) based deep
learning framework for graph drawing, called SmartGD, which can optimize
different quantitative aesthetic goals, regardless of their differentiability.
To demonstrate the effectiveness and efficiency of SmartGD, we conducted
experiments on minimizing stress, minimizing edge crossing, maximizing crossing
angle, maximizing shape-based metrics, and a combination of multiple
aesthetics. Compared with several popular graph drawing algorithms, the
experimental results show that SmartGD achieves good performance both
quantitatively and qualitatively.

Machine Learning