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Paper detail

New Quality Metrics for Dynamic Graph Drawing

In this paper, we present new quality metrics for dynamic graph drawings. Namely, we present a new framework for change faithfulness metrics for dynamic graph drawings, which compare the ground truth change in dynamic graphs and the geometric change in drawings. More specifically, we present two specific instances, cluster change faithfulness metrics and distance change faithfulness metrics. We first validate the effectiveness of our new metrics using deformation experiments. Then we compare various graph drawing algorithms using our metrics. Our experiments confirm that the best cluster (resp. distance) faithful graph drawing algorithms are also cluster (resp. distance) change faithful.

preprint2020arXivOpen access
Amyra MeidianaSeok-Hee HongPeter Eades
Social and Information NetworksHuman-Computer InteractionData Structures and Algorithms
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Amyra MeidianaSeok-Hee HongPeter Eades

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