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Aesthetic discrimination of graph layouts

: Klammler, M.; Mchedlidze, T.; Pak, Alexey

Volltext (PDF; )

Biedl, Therese (Ed.):
Graph drawing and network visualization. 26th International Symposium, GD 2018 : Barcelona, Spain, September 26-28, 2018; Proceedings
Cham: Springer Nature, 2018 (Lecture Notes in Computer Science 11282)
ISBN: 978-3-030-04413-8 (Print)
ISBN: 978-3-030-04414-5 (Online)
ISBN: 978-3-030-04415-2
International Symposium on Graph Drawing and Network Visualization (GD) <26, 2018, Barcelona>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
graph drawing; graph drawing aesthetics; machine learning; neural network; graph drawing syndromes

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. Our model demonstrates a mean prediction accuracy of 96.48%, outperforming discriminators based on stress and on the linear combination of popular quality metrics by a small but statistically significant margin.