A Quality Metric for Visualization of Clusters in Graphs

TL;DR

This paper introduces a new graph visualization quality metric that quantifies how effectively a graph drawing reveals its cluster structure, validated through experiments and comparisons of different algorithms.

Contribution

It defines the first explicit metric for evaluating how well graph drawings represent cluster structures, filling a gap in graph visualization quality assessment.

Findings

The metric effectively captures visual cluster quality variations.

Algorithms designed for cluster visualization outperform others.

Experiments confirm the metric's usefulness in evaluating graph drawings.

Abstract

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.