Authors: Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, Huamin Qu
Abstract: 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. Such a trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the excellent capability of deep learning techniques in data modeling and prediction, we aim at exploring the possibility of applying deep learning techniques to graph drawing. Specifically, we propose 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 of a similar style for new networks. We evaluated the proposed approach on two regular drawings (i.e., grid layout and star layout) and two general drawings (i.e., ForceAtlas2 and PivotMDS) in both qualitative and quantitative ways, which provides support for the effectiveness of our approach. A time cost assessment on the drawings of small graphs with 20 to 50 nodes is also conducted. We further report the lessons we learned and discuss its limitations and future work.