In IEEE computer graphics and applications
In the past, 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 making compromise between multiple pre-specified aesthetics because a good graph layout usually complies with multiple aesthetics simultaneously. In order to balance the trade-off among aesthetics, 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 effective for drawing arbitrary graphs, while being flexible at accommodating different aesthetic criteria.
Wang Xiaoqi, Yen Kevin, Hu Yifan, Shen Han-Wei