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Shape-Based Quality Metrics for Large Graph Visualization

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2017

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Eades, Peter
Hong, Seok-Hee
Nguyen, An

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https://doi.org/10.7155/jgaa.00405
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Journal of Graph Algorithms and Applications : JGAA. 2017, 21(1), pp. 29-53. eISSN 1526-1719. Available under: doi: 10.7155/jgaa.00405

Zusammenfassung

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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ISO 690EADES, Peter, Seok-Hee HONG, An NGUYEN, Karsten KLEIN, 2017. Shape-Based Quality Metrics for Large Graph Visualization. In: Journal of Graph Algorithms and Applications : JGAA. 2017, 21(1), pp. 29-53. eISSN 1526-1719. Available under: doi: 10.7155/jgaa.00405
BibTex
@article{Eades2017Shape-44581,
  year={2017},
  doi={10.7155/jgaa.00405},
  title={Shape-Based Quality Metrics for Large Graph Visualization},
  number={1},
  volume={21},
  journal={Journal of Graph Algorithms and Applications : JGAA},
  pages={29--53},
  author={Eades, Peter and Hong, Seok-Hee and Nguyen, An and Klein, Karsten}
}
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