Towards Partially Automatic Search of Edge Bundling Parameters

Edge bundling methods are used in flow maps and graphs to reduce the visual clutter, which is generated when representing complex and heterogeneous data. Nowadays, there are many edge bundling algorithms that have been successfully applied to a wide range of problems in graph representation. However, the majority of these methods are still difficult to use and apply on real world problems by the experts from other areas. This is due to the complexity of the algorithms and concepts behind them, as well as a strong dependence on their parametrization. In addition, the majority of edge bundling methods need to be fine-tuned when applied on different datasets. This paper presents a new approach that helps finding near-optimal parameters for solving such issues in edge bundling algorithms, regardless of the configuration of the input graph. Our method is based on evolutionary computation, allowing the users to find edge bundling solutions for their needs. In order to understand the effectiveness of the evolutionary algorithm in such kind of tasks, we performed experiments with automatic fitness functions, as well as with partially user-guided evolution. We tested our approach in the optimization of the parameters of two different edge bundling algorithms. Results are compared using objective criteria and a critical discussion of the obtained graphical solutions.