New Quality Metrics for Dynamic Graph Drawing
This work addresses the problem of evaluating dynamic graph visualizations for researchers and practitioners in graph drawing, but it is incremental as it builds on existing metrics.
The paper introduces new quality metrics for dynamic graph drawings, specifically cluster and distance change faithfulness metrics, and validates their effectiveness through deformation experiments and comparisons of various graph drawing algorithms.
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.