Same Quality Metrics, Different Graph Drawings

TL;DR

This paper demonstrates that existing graph quality metrics can be manipulated to produce arbitrary drawings with similar metric scores, highlighting the need for more reliable and comprehensive quality measures in graph visualization.

Contribution

It explicitly shows that current quality metrics can be deceived, emphasizing the necessity for developing more accurate metrics for graph drawing evaluation.

Findings

Existing metrics can be manipulated to produce different drawings with similar scores.

Current quality metrics may not reliably reflect the true quality of graph drawings.

More advanced and comprehensive metrics are needed for accurate evaluation.

Abstract

Graph drawings are commonly used to visualize relational data. User understanding and performance are linked to the quality of such drawings, which is measured by quality metrics. The tacit knowledge in the graph drawing community about these quality metrics is that they are not always able to accurately capture the quality of graph drawings. In particular, such metrics may rate drawings with very poor quality as very good. In this work we make this tacit knowledge explicit by showing that we can modify existing graph drawings into arbitrary target shapes while keeping one or more quality metrics almost identical. This supports the claim that more advanced quality metrics are needed to capture the 'goodness' of a graph drawing and that we cannot confidently rely on the value of a single (or several) certain quality metrics.