1 research outputs found
DeepDrawing: A Deep Learning Approach to Graph Drawing
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.Comment: 11 page