Visualising web browsing data for user behaviour analysis
Proceedings of the 23rd Australian Computer-Human Interaction Conference
Evaluating force-directed algorithms with a new framework
Proceedings of the 27th Annual ACM Symposium on Applied Computing
An aggregation-based approach to quality evaluation of graph drawings
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
Improving multiple aesthetics produces better graph drawings
Journal of Visual Languages and Computing
| Hi-index | 0.00 |
Many automatic graph drawing algorithms implement only one or two aesthetic criteria since most aesthetics conflict with each other. Empirical research has shown that although those algorithms are based on different aesthetics, drawings produced by them have comparable effectiveness. The comparable effectiveness raises a question about necessity of choosing one algorithm against another for drawing graphs when human performance is a main concern. In this paper, we argue that effectiveness can be improved when algorithms are designed by making compromises between aesthetics, rather than trying to satisfy one or two of them to the fullest. In particular, this paper presents a user study. The study compares effectiveness of drawings produced by two different force-directed methods, Classical spring algorithm and BIGANGLE. BIGANGLE produces drawings with a few aesthetics being improved at the same time. The experimental results indicate that BIGANGLE induces significantly better performance of humans in perceiving shortest paths between two nodes.