Same Quality Metrics, Different Graph Drawings
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915455660195840 |
|---|---|
| author | van Wageningen, Simon Mchedlidze, Tamara Telea, Alexandru C. |
| author_facet | van Wageningen, Simon Mchedlidze, Tamara Telea, Alexandru C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15557 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Same Quality Metrics, Different Graph Drawings van Wageningen, Simon Mchedlidze, Tamara Telea, Alexandru C. Computational Geometry 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. |
| title | Same Quality Metrics, Different Graph Drawings |
| topic | Computational Geometry |
| url | https://arxiv.org/abs/2508.15557 |