  In the past decades, many graph drawing techniques have been proposed for
generating aesthetically pleasing graph layouts. However, it remains a
challenging task since different layout methods tend to highlight different
characteristics of the graphs. Recently, studies on deep learning based graph
drawing algorithm have emerged but they are often not generalizable to
arbitrary graphs without re-training. In this paper, we propose a Convolutional
Graph Neural Network based deep learning framework, DeepGD, which can draw
arbitrary graphs once trained. It attempts to generate layouts by compromising
among multiple pre-specified aesthetics considering a good graph layout usually
complies with multiple aesthetics simultaneously. In order to balance the
trade-off, we propose two adaptive training strategies which adjust the weight
factor of each aesthetic dynamically during training. The quantitative and
qualitative assessment of DeepGD demonstrates that it is capable of drawing
arbitrary graphs effectively, while being flexible at accommodating different
aesthetic criteria.
