A Quality Metric for Visualization of Clusters in Graphs

  • Amyra MeidianaEmail author
  • Seok-Hee Hong
  • Peter Eades
  • Daniel Keim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11904)


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.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Amyra Meidiana
    • 1
    Email author
  • Seok-Hee Hong
    • 1
  • Peter Eades
    • 1
  • Daniel Keim
    • 2
  1. 1.University of SydneySydneyAustralia
  2. 2.University of KonstanzKonstanzGermany

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