New edge bundling taxonomy helps visualize complex networks clearly

Edge bundling is a popular technique to reduce visual clutter in network visualizations by grouping similar edges together. However, practitioners have lacked a structured vocabulary to reason about the specific tasks these bundled visualizations support. Markus Wallinger and Stephen G. Kobourov address this gap in their new paper "A Task Taxonomy for Edge and Trail Bundling," accepted at IEEE VIS 2026. They assembled a corpus of 102 papers, 49 of which contain explicit bundling tasks, spanning node-link diagrams, geographic trail sets, and parallel coordinate plots. From this corpus, they derive a task taxonomy organized as a matrix of scope (Element, Bundle, Global, Multi-view) crossed with action (Verify, Identify, Characterize, Quantify, Compare, Assess), instantiated across the three representation types. A key finding is that bundling simultaneously enables tasks such as bundle-level and global reasoning, while disabling others like element-level precision — a duality not captured by existing task frameworks.

This taxonomy provides a systematic way to evaluate the general utility of edge bundling and to compare different bundling approaches. For visualization designers and researchers, it offers a clear language to articulate what tasks their bundled visualizations support or hinder. The authors have released their coded corpus and taxonomy as supplemental material on the Open Science Framework, allowing others to build upon their work. The framework is particularly relevant for data analysts and HCI practitioners working with complex network data, geographic trails, or multi-dimensional datasets visualized with parallel coordinates. By understanding the trade-offs revealed by the taxonomy, professionals can make more informed choices about when and how to apply edge bundling. The paper received a short paper presentation at IEEE VIS, confirming its relevance to the visualization community.