  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.

Data Structures and Algorithms;Human-Computer Interaction;Social and Information Networks