Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of layout examples as the training dataset, we train the proposed graph-LSTM-based model to capture their layout characteristics. Then, the trained model is used to generate graph drawings in a similar style for new networks. We evaluated the proposed approach on two special types of layouts (i.e., grid layouts and star layouts) and two general types of layouts (i.e., ForceAtlas2 and PivotMDS) in both qualitative and quantitative ways. The results provide support for the effectiveness of our approach. We also conducted a time cost assessment on the drawings of small graphs with 20 to 50 nodes. We further report the lessons we learned and discuss the limitations and future work.

中文翻译：

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DeepDrawing：图形学习的深度学习方法。

节点链接图被广泛用于促进网络探索。但是，当使用图形绘制技术来可视化网络时，用户通常需要通过比较相应的绘制结果来迭代地调整不同的算法特定参数，以实现所需的视觉效果。这种反复试验的过程通常很繁琐且耗时，特别是对于非专业用户而言。受深度学习技术强大的数据建模和预测功能的启发，我们探索了将深度学习技术应用于图形绘制的可能性。具体来说，我们建议使用基于图LSTM的方法将网络结构直接映射到图。给定一组布局示例作为训练数据集，我们对建议的基于图形LSTM的模型进行训练以捕获其布局特征。然后，训练后的模型用于为新网络以类似样式生成图形绘图。我们以定性和定量方式对两种特殊类型的布局（即网格布局和星形布局）和两种常规类型的布局（即ForceAtlas2和PivotMDS）进行了评估。结果为我们方法的有效性提供了支持。我们还对具有20到50个节点的小图的图形进行了时间成本评估。我们进一步报告所学到的教训，并讨论局限性和未来的工作。结果为我们方法的有效性提供了支持。我们还对具有20到50个节点的小图的图形进行了时间成本评估。我们进一步报告所学到的教训，并讨论局限性和未来的工作。结果为我们方法的有效性提供了支持。我们还对具有20到50个节点的小图的图形进行了时间成本评估。我们进一步报告所学到的教训，并讨论局限性和未来的工作。