DeepGD: A Deep Learning Framework for Graph Drawing Using GNN
This work addresses the problem of generalizable graph drawing for researchers and practitioners, but it is incremental as it builds on existing deep learning methods with adaptive strategies.
The authors tackled the challenge of generating aesthetically pleasing graph layouts that generalize to arbitrary graphs without retraining, by proposing DeepGD, a GNN-based framework that balances multiple aesthetic criteria using adaptive training strategies, achieving effective and flexible graph drawing.
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.