Quality metrics are significant simply because they measure success or failure of a graph
drawing method. Most importantly, they are used as optimisation goals in designing graph drawing
This talk will present two new family of quality metrics for graph drawing: faithfulness and
We illustrate these metrics with examples, and apply the metrics to data from previous
experiments, leading to the suggestion that the new metrics are effective.
Seokhee Hong is a professor and ARC Future Fellow at the University of Sydney. She was a
Humboldt Fellow, ARC Research Fellow and a project leader of VALACON (Visualisation and Analysis of
Large and Complex Networks) project at NICTA (National ICT Australia).
Her research interests include Graph Drawing, Algorithms, Information Visualisation and Visual
She serves as a Steering Committee member of IEEE PacificVis (International Symposium on Pacific
Visualisation) and ISAAC (International Symposium on Algorithms and Computations), and an editor of
JGAA (Journal of Graph Algorithms and Applications) and IEEE Compute Graphics and Applications.