# A Study of Colormaps in Network Visualization
## 1 Introduction  
Color is commonly used for the visual encoding of data in many visualization designs. A colormap specifically refers to a mapping from data values to colors. It is crucial to use the appropriate colormap for the data under study, as a _good_ colormap helps viewers to understand and communicate with the data, while a bad colormap can mislead the viewers in terms of understanding the data. Researchers have used several types of colormaps for data visualization such as _blue_ (single-hue, sequential), _RAYIBu_ (diverging, red-yellow-blue), _viridis_ (multi-hue, sequential), and _jet_ (rainbow), as shown in Figure 1.  
Visualization designers select a colormap either based on their intuition and experience or based on recommendations from color science [1]. The effectiveness of a colormap depends on various factors [2]. For example, an effective colormap for encoding ordered data often uses luminance and saturation to encode the values, since hue does not have an intuitive ordering [3]. In addition, the effect of a colormap for a given dataset can differ depending on the task [4]. Furthermore, different mark types can affect the perception of color differences [1].  
While several studies have investigated the effects of colormaps in various types of data visualization, no study has examined their use in network visualization. Network visualization is widely used in a variety of domains, such as social [5] and biological sciences [6; 7; 8]. There are multiple approaches available for visualizing networks, such as the visual matrix, node-link diagram, and space layout [9]. Of these, the node-link diagram, which depicts a set of entities (nodes) as points and the relationship among them (links) as lines, is the most popular technique.  
The goal of this study is to investigate the effects of four different colormaps for encoding quantitative node attributes in a node-link diagram. The colormaps (Figure 1) considered for the evaluation are _blue_ (single-hue, sequential), _RdYIBu_ (divergent), _viridis_ (multi-hue, sequential, perceptually uniform), and _jet_ (rainbow). Additionally, we use structural and non-structural node attributes to see how the effectiveness of colormaps differs depending on the type of node attribute assessed. We analyze the task completion time and correctness rate of participants with each colormap in a node-link diagram.  
The results of the user study show that the _blue_ (single-hue, sequential) colormap performs faster than the other three multi-hue colormaps with respect to task completion time. However, in terms of correctness rate, _blue_ is the most error-prone, while _viridis_ (multi-hue, sequential, perceptually uniform) shows the most accurate results. We find that colormaps can perform differently when used in real visualizations as compared with their evaluation in an isolated setting without any given context or visualization. Thus, this study establishes a more in-depth understanding of colormaps in network visualization and also gives several guidelines that can be used by visualization designers to achieve effective color encoding for visualizing networks.