# A Quality Metric for Visualization of Clusters in Graphs

arXiv: https://arxiv.org/abs/1908.07792 · HTML: https://mlsanity.com/paper/1908.07792/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [A Machine Learning Approach for Predicting Human Preference for Graph Layouts](https://mlsanity.com/paper/2103.03665/) (2021)

## Abstract (arXiv)

Traditionally, graph quality metrics focus on readability, but recent studies show the need for metrics which are more specific to the discovery of patterns in graphs. Cluster analysis is a popular task within graph analysis, yet there is no metric yet explicitly quantifying how well a drawing of a graph represents its cluster structure. We define a clustering quality metric measuring how well a node-link drawing of a graph represents the clusters contained in the graph. Experiments with deforming graph drawings verify that our metric effectively captures variations in the visual cluster quality of graph drawings. We then use our metric to examine how well different graph drawing algorithms visualize cluster structures in various graphs; the results con-firm that some algorithms which have been specifically designed to show cluster structures perform better than other algorithms.