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Aesthetic discrimination of graph layouts

 
: McHedlidze, T.; Pak, A.; Klammler, M.

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Fulltext (PDF; )

Journal of graph algorithms and applications 23 (2019), No.3, pp.525-552
ISSN: 1526-1719
International Symposium on Graph Drawing and Network Visualization (GD) <26, 2018, Barcelona>
English
Journal Article, Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Abstract
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.

: http://publica.fraunhofer.de/documents/N-630469.html