Overlaying Quantitative Measurement on Networks: An Evaluation of Three Positioning and Nine Visual Marker Techniques

TL;DR

This study evaluates nine visual markers and three node positioning techniques in network visualizations, finding that direct encoding improves accuracy and that hue and area are most effective for comparison tasks.

Contribution

It provides a comprehensive ranking of visual markers and positioning methods for network visualization comparison tasks, especially across different scales.

Findings

Direct encoding of quantities improves accuracy by about 20%.

Hue and area are most effective for comparison tasks.

Circular positioning consistently performs well across tasks.

Abstract

We report results from an experiment on ranking visual markers and node positioning techniques for network visualizations. Inspired by prior ranking studies, we rethink the ranking when the dataset size increases and when the markers are distributed in space. Centrality indices are visualized as node attributes. Our experiment studies nine visual markers and three positioning methods. Our results suggest that direct encoding of quantities improves accuracy by about 20% compared to previous results. Of the three positioning techniques, circular was always in the top group, and matrix and projection switch orders depending on two factors: whether or not the tasks demand symmetry, or the nodes are within closely proximity. Among the most interesting results of ranking the visual markers for comparison tasks are that hue and area fall into the top groups for nearly all multi-scale comparison tasks; Shape (ordered by curvature) is perhaps not as scalable as we have thought and can support more accurate answers only when two quantities are compared; Lightness and slope are least accurate for quantitative comparisons regardless of scale of the comparison tasks. Our experiment is among the first to acquire a complete picture of ranking visual markers in different scales for comparison tasks.