Bundling-Aware Graph Drawing Revisited

Abstract

Edge bundling algorithms can significantly improve the visualization of dense graphs by identifying and bundling together suitable groups of edges and thus reducing visual clutter. As such, bundling is often viewed as a post-processing step applied to a drawing, and the vast majority of edge bundling algorithms consider a graph and its drawing as input. A different way of thinking about edge bundling is to simultaneously optimize both the drawing and the bundling, which we investigate in this paper. We build on an earlier work where we introduced a novel algorithmic framework for bundling-aware graph drawing consisting of three main steps, namely Filter for a skeleton subgraph, Draw the skeleton, and Bundle the remaining edges against the drawing of the skeleton. We propose several alternative implementations and experimentally compare them against each other and the simple idea of first drawing the full graph and subsequently applying edge bundling to it. The experiments confirm that bundled drawings created by our Filter-Draw-Bundle framework outperform previous approaches according to metrics for edge bundling and graph drawing.