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 algorithms.This talk will present two new family of quality metrics for graph drawing: faithfulness and shape-based metrics. 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 at the University of Sydney. She was an ARC Future Fellow, 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 Analytics. 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.