Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off

TL;DR

Peacock Bundles introduces an edge coloring optimization method that balances local and global differentiation to improve the interpretability of bundled graph layouts.

Contribution

It proposes a novel, interpretable optimization approach for edge coloring that enhances graph visualization by managing local-global bundling trade-offs.

Findings

Coloring improves graph layout clarity.

The method effectively balances local and global bundling differentiation.

Experiments confirm enhanced comprehensibility of visualized graphs.

Abstract

Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual edges. We propose a solution: we optimize edge coloring to differentiate bundled edges. We quantify strength of bundling in a flexible pairwise fashion between edges, and among bundled edges, we quantify how dissimilar their colors should be by dissimilarity of their origins and destinations. We solve the resulting nonlinear optimization, which is also interpretable as a novel dimensionality reduction task. In large graphs the necessary compromise is whether to differentiate colors sharply between locally occurring strongly bundled edges ("local bundles"), or also between the weakly bundled edges occurring globally over the graph ("global bundles"); we allow a user-set global-local tradeoff. We call the technique "peacock bundles". Experiments show the coloring clearly enhances comprehensibility of graph layouts with edge bundling.