Aesthetic Discrimination of Graph Layouts
This addresses the need for automated aesthetic evaluation in graph visualization, though it is incremental as it builds on existing metrics and methods.
The paper tackled the problem of automatically determining which of two graph layouts is more aesthetically pleasing by proposing a neural network discriminator trained on a dataset of layout pairs, achieving a mean prediction accuracy of 95.70%.
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.