Shape-Based Quality Metrics for Large Graph Visualization
Peter Eades, Seok-Hee Hong, An Nguyen, and Karsten Klein
Vol. 21, no. 1, pp. 29-53, 2017. Regular paper.
Abstract 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.
Submitted: January 2016.
Reviewed: May 2016.
Revised: August 2016.
Reviewed: November 2016.
Revised: December 2016.
Accepted: December 2016.
Final: December 2016.
Published: January 2017.
Communicated by Emilio Di Giacomo and Anna Lubiw
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