New Quality Metrics for Dynamic Graph Drawing
Abstract
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.
Cite
@article{arxiv.2008.07764,
title = {New Quality Metrics for Dynamic Graph Drawing},
author = {Amyra Meidiana and Seok-Hee Hong and Peter Eades},
journal= {arXiv preprint arXiv:2008.07764},
year = {2020}
}
Comments
Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)