Geodesic
Aesthetic Discrimination of Graph Layouts
Journal of Graph Algorithms and Applications, Special issue on Selected papers from the Twenty-sixth International Symposium on Graph Drawing and Network Visualization, GD 2018 , Tome 23 (2019) no. 3, pp. 525-552.

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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 $97.58\%$, outperforming discriminators based on stress and on the linear combination of popular quality metrics by a margin of $2$ to $3\%$. The present paper extends our contribution to the Proceedings of the 26th International Symposium on Graph Drawing and Network Visualization (GD 2018) and is based on a significantly larger dataset.
DOI : 10.7155/jgaa.00501
Keywords: aesthetics, graph drawing, machine learning, quality metrics 
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Tamara Mchedlidze; Alexey Pak; Moritz Klammler. Aesthetic Discrimination of Graph Layouts. Journal of Graph Algorithms and Applications, 
							Special issue on Selected papers from the Twenty-sixth International Symposium on Graph Drawing and Network Visualization, GD 2018
					, Tome 23 (2019) no. 3, pp. 525-552. doi : 10.7155/jgaa.00501. http://geodesic.mathdoc.fr/articles/10.7155/jgaa.00501/

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