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Aesthetic discrimination of graph layouts. (English) Zbl 1419.05148
Summary: 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 [the authors, Lect. Notes Comput. Sci. 11282, 169–184 (2018; Zbl 07023823)] and is based on a significantly larger dataset.
05C62 Graph representations (geometric and intersection representations, etc.)
MatrixMarket; OGDF
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