Visualizing Evolving Trees

TL;DR

This paper introduces two novel visualization methods for evolving trees that ensure no edge crossings while optimizing layout stability, compactness, and edge length, outperforming prior approaches in real-world datasets.

Contribution

The paper proposes two new crossing-free evolving tree visualization algorithms that improve stability, compactness, and edge length realization, validated through comprehensive evaluation.

Findings

The new methods guarantee no edge crossings in evolving tree visualizations.

They outperform prior static and dynamic approaches in stability and layout quality.

The methods are fully functional and available on GitHub.

Abstract

Evolving trees arise in many real-life scenarios from computer file systems and dynamic call graphs, to fake news propagation and disease spread. Most layout algorithms for static trees do not work well in an evolving setting (e.g., they are not designed to be stable between time steps). Dynamic graph layout algorithms are better suited to this task, although they often introduce unnecessary edge crossings. With this in mind we propose two methods for visualizing evolving trees that guarantee no edge crossings, while optimizing (1) desired edge length realization, (2) layout compactness, and (3) stability. We evaluate the two new methods, along with five prior approaches (three static and two dynamic), on real-world datasets using quantitative metrics: stress, desired edge length realization, layout compactness, stability, and running time. The new methods are fully functional and available on github.