  This paper addresses the following basic question: given two layouts of the
same graph, which one is more aesthetically pleasing? We propose a neural
network-based discriminator model trained on a labeled dataset that decides
which of two layouts has a higher aesthetic quality. The feature vectors used
as inputs to the model are based on known graph drawing quality metrics,
classical statistics, information-theoretical quantities, and two-point
statistics inspired by methods of condensed matter physics. The large corpus of
layout pairs used for training and testing is constructed using force-directed
drawing algorithms and the layouts that naturally stem from the process of
graph generation. It is further extended using data augmentation techniques.
The mean prediction accuracy of our model is 95.70%, outperforming
discriminators based on stress and on the linear combination of popular quality
metrics by a statistically significant margin.
