Different layouts can characterize different aspects of the same graph. Finding a “good” layout of a graph is thus animportant task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods andvarying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error processis often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we presenta technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoderarchitecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent spaceand the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent spacefor users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluationsof the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract conceptsof graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graphvisualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics. Read More

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