A Study of Colormaps in Network Visualization

Abstract

Color is a widely used visual channel for encoding data in visualization design. It is important to select the appropriate type of color mapping to better understand the data. While several studies have investigated the effects of colormaps in various types of information visualization, there have been no studies on their effects on network visualization. Thus, in this paper, we investigate the effects of several colormaps in network visualization using node-link diagrams. Specifically, we compare four different single- and multi-hue colormaps for node attributes, and evaluate their effectiveness in terms of task completion time and correctness rate. Our results show that participants complete their tasks significantly faster with blue (single-hue, sequential) as compared to viridis (multi-hue, sequential), RdYlBu (divergent, red-yellow-blue), and jet (rainbow) colormaps. Additionally, the overall correctness rate shows significant differences between colormaps, with viridis being the least error-prone among the colormaps studied.

A network approach to genetic circuit designs

“…By presenting a single perspective, overall cognition is increased, but it is visually incoherent since the position of nodes–its layout —is random. The layout, which determines the arrangement of nodes combined with other features such as colour, size and shape, 40 provides a visual representation of information that ensures clarity and understanding. When a simple radial layout (Figure 1D) (e.g., nodes do not overlap) is combined with node clustering and colouring depending on types and roles, it results in a final visual output that is considerably clearer than fig:Figure 1B.…”

The State of the Art in Empirical User Evaluation of Graph Visualizations

“…It was found that task performance varied with visual complexity of visualizations and task complexity. The impact of color coding was investigated by Karim et al [140] for node-link diagrams (see Figure 10) by recruiting 35 participants. Blue colormaps seem to perform much better as well as viridis, producing fewer errors.…”

“…Also, difference maps as visual enrichment in dynamic graphs are beneficial to detect changes [48]. Typical visual variables like size and color [140], [142] are suitable visual enrichments, contour lines produce quite good effects [144], as well as motifs [143]. Moreover, group indications [145], [148], also shown by colored regions [146], are suitable features, but background color was found better than node color [147].…”

Wind Velocity Data Interpolation Using Rational Cubic Spline

“…Their method is only capable to approximate the wind velocity where the interpolations to all data points are lost. Karim [4] discussed the monotonic interpolating cure by using rational cubic spline of Karim and Kong [3]. Sarfraz et al [6] discussed the positivity preserving interpolation by using rational cubic spline with two parameters.…”

“…Condition (4) is the same as 0the sufficient condition for the positivity of the rational cubic interpolant defined by(3). For strictly positive data, the rational cubic spline interpolant ( )…”