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Axioms 2017, 6(2), 12; doi:10.3390/axioms6020012

Toward Measuring Network Aesthetics Based on Symmetry

1
College of Computer and Control Engineering, Nankai University, Tianjin 300350, China
2
Department of Mechatronics and Biomedical Computer Science, University for Health Sciences,Medical Informatics and Technology (UMIT), 6060 Hall, Tyrol, Austria
3
Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology,33720 Tampere, Finland
4
Department of Computer Science, The City College of New York (CUNY), 138th Street at Convent Avenue,New York, NY 10031, USA
5
Center for Combinatorics and LPMC, Nankai University, Tianjin 300071, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Javier Fernandez
Received: 20 March 2017 / Revised: 3 May 2017 / Accepted: 3 May 2017 / Published: 6 May 2017
(This article belongs to the Special Issue Entropy and Information Theory)
View Full-Text   |   Download PDF [695 KB, uploaded 9 May 2017]   |  

Abstract

In this exploratory paper, we discuss quantitative graph-theoretical measures of network aesthetics. Related work in this area has typically focused on geometrical features (e.g., line crossings or edge bendiness) of drawings or visual representations of graphs which purportedly affect an observer’s perception. Here we take a very different approach, abandoning reliance on geometrical properties, and apply information-theoretic measures to abstract graphs and networks directly (rather than to their visual representaions) as a means of capturing classical appreciation of structural symmetry. Examples are used solely to motivate the approach to measurement, and to elucidate our symmetry-based mathematical theory of network aesthetics. View Full-Text
Keywords: aesthetics; networks; entropy; aesthetical theory; network aesthetics aesthetics; networks; entropy; aesthetical theory; network aesthetics
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Chen, Z.; Dehmer, M.; Emmert-Streib, F.; Mowshowitz, A.; Shi, Y. Toward Measuring Network Aesthetics Based on Symmetry. Axioms 2017, 6, 12.

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