A Quality Metric for Visualization of Clusters in Graphs

• Published in: Graph Drawing and Network Visualization Vol. 11904; pp. 125 - 138
• Main Authors: Meidiana, Amyra, Hong, Seok-Hee, Eades, Peter, Keim, Daniel
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 3030358011; 9783030358013
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-35802-0_10

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 confirm that some algorithms which have been specifically designed to show cluster structures perform better than other algorithms.