New Quality Metrics for Dynamic Graph Drawing

TL;DR

This paper introduces new metrics for evaluating the quality of dynamic graph drawings by measuring how well the visual changes reflect actual graph changes, validated through experiments and algorithm comparisons.

Contribution

It proposes a novel framework for change faithfulness metrics in dynamic graph visualization, including specific cluster and distance change metrics, and validates their effectiveness.

Findings

New metrics effectively measure change faithfulness in dynamic graphs

Best algorithms are consistent with the proposed change faithfulness metrics

Metrics validated through deformation experiments and algorithm comparisons

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.