Improving Force-Directed Graph Drawings by Making Compromises Between Aesthetics

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.