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Quantitative Metrics for Edge Bundling of Network Visualizations
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DOI：10.1111/cgf.70443.png)
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Abstract
En 中文
Edge bundling is widely used for reducing visual clutter in large 2D network and trajectory visualizations. Various edge bundling methods have been proposed, each producing qualitatively distinct outputs for the same data; however, few quantitative metrics exist for systematic evaluation. In this paper, we propose a set of quantitative metrics at the edge level (e.g., geometric properties of the resulting curves), the bundle level (e.g., thickness and number of bundles), and the global level (e.g., ambiguity and clustering). We propose a benchmark of 115 representative datasets and evaluate five representative edge bundling techniques that cover a broad range of methodological approaches. We also conduct a correlation analysis between bundling metrics and network drawing properties. To facilitate further analysis and comparison, we provide an interactive dashboard that includes all methods, metrics, and datasets, enabling side-by-side exploration of edge bundling effects. All supplemental materials and a link to our dashboard application are available at OSF.
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CCS Concepts
• Human-centered computing → Visualization techniques
Graph drawings
Empirical studies in visualization
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