Optimization of Hierarchical Graph Layout with a Genetic Algorithm and Sprawl/Clutter Metrics

Graph layouts are useful for visualizing relationships between data entities. Nodes represent each entity, and edges represent the relationships between the nodes. Hierarchical graph visualization is known as an efficient method to represent the overview of large-scale graphs, where some clusters are generated depending on node properties and are drawn as a set of multiple nodes (meta-nodes). However, it is often difficult to determine the layouts of nodes and edges of large-scale graphs, including hierarchical graphs. Although many graph layout methods have been proposed so far, the quality of the layouts by many of such methods including force-directed layout methods strongly depends on the initial positions of the layouts. Therefore, it is often a difficult task to obtain desired layouts. This paper presents a layout optimization method for hierarchical graphs using a genetic algorithm. Specific metrics for hierarchical graph layouts are used to evaluate the generated layout. This method makes users easy to select a favorite layout from several layouts which are optimized based on the evaluation results. First, hierarchical graph layouts are generated by applying an existing method multiple times. Then, they are optimized using a genetic algorithm. Users can select their favorite layouts from several optimized layouts. Consequently, this method does not require adjusting initial positions of the layouts to obtain good layouts. The paper also introduces examples using human relationship graph datasets, including a co-authorship dataset.