Geodesic
Shape-Based Quality Metrics for Large Graph Visualization
Journal of Graph Algorithms and Applications, Special Issue on Selected papers from the Twenty-third International Symposium on Graph Drawing and Network Visualization, GD 2015 , Tome 21 (2017) no. 1, pp. 29-53.

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The scalability of graph layout algorithms has gradually improved for many years. However, only recently a discussion has started to investigate the usefulness of established quality metrics, such as the number of edge crossings, in the context of increasingly larger graphs stemming from a variety of application areas such as social network analysis or biology. Initial evidence suggests that the traditional metrics are not well suited to capture the quality of corresponding graph layouts. We propose a new family of quality metrics for graph drawing; in particular, we concentrate on larger graphs. We illustrate these metrics with examples and apply the metrics to data from previous experiments, leading to the suggestion that the new metrics are effective. 
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Peter Eades; Seok-Hee Hong; An Nguyen; Karsten Klein. Shape-Based Quality Metrics for Large Graph Visualization. Journal of Graph Algorithms and Applications, 
							Special Issue on Selected papers from the Twenty-third International Symposium on Graph Drawing and Network Visualization, GD 2015
					, Tome 21 (2017) no. 1, pp. 29-53. doi : 10.7155/jgaa.00405. http://geodesic.mathdoc.fr/articles/10.7155/jgaa.00405/

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