# A Study of Colormaps in Network Visualization
## 2 Related Work
### Colormaps and Evaluation Studies  
Mapping data to visual channels (e.g., position, size, and color) can reveal unseen patterns and insights in the data. In many existing studies (e.g., [17; 19; 20; 21]), it is well documented that among the different visual channels, color is more powerful than size, shape, and orientation [22]. These studies also characterize the properties of a color scale that result in useful and effective colormaps [1]. There are three important properties of colormaps: discriminative power, uniformity, and order [23]. Discriminative power depicts the number of specific colors that viewers can identify on a color scale. Uniformity pertains to the consistency in perceived variations between pairs of equidistant areas sampled  
Figure 1: The colormaps used in this study: (**a**) _blue_ (single-hue, sequential); (**b**) _RdYIBu_ (diverging); (**c**) _viridis_ (multi-hue, sequential, perceptually uniform); and (**d**) _jet_ (rainbow). from various parts of a color scale. Order relates to the appearance that colors in the color scale follow in a natural progression. Color encoding is a fundamental way of representing scalar values in visualizations, and therefore, it is used in a wide range of application scenarios [4]. Currently, designers develop colormaps based on their experience, cognitive heuristics, and empirical data from several experiments [24]. [PERSON]'s ColorBrewer [25], a pioneering work in color encoding, explains the binary, qualitative, sequential, and diverging color scales used for visualization on cartographic maps [4]. In addition, survey studies have focused on the design of various colormaps and provided guidance on effective color encoding [2; 26]. [PERSON] and [PERSON] [2] reviewed the systems used to generate colormaps based on perception and cognitive heuristics. For example, PRAVDAColor considers heuristics for color encoding based on perception, task, and data type [27]. In addition to using perceptual color space for encoding, algorithms have also been used to generate colormaps [28]. For example, Colorgorical [29] uses scores of perceptual distances, color names, and aesthetics models to generate categorical palettes, and ColorBrewer [30] schemes are modeled based on color theory and are then hand-tuned for aesthetics and performance. The effectiveness of a colormap depends on the data and task at hand [2; 26; 31]; thus, one must consider the type of data in use before encoding to a certain colormap. Studies show that sequential colormaps best represent sequential data, whereas diverging data with a neutral or average midpoint is best suited for diverging colormaps [22]. [PERSON] et al. [4] designed a color mapping function to support tasks like comparison, localization, or identification of data values. ColorCAT [32] expanded the scope of this work by adding localization and identification tasks as well as colorblindness assessment. Several tools have been developed to help users design colormaps. For example, [PERSON] et al. [27] developed a rule-based approach that takes spatial frequency into account, [PERSON] and McGill [33] showed that different color channels communicate individual values less precisely than position or size, and [PERSON] et al. [34] compared the effectiveness of color and other channels for investigating uncertainty. Other aspects of colormap design that can affect their efficacy have also been studied, such as color naming and semantics [35; 36], categorical similarity, and cognitive bias [1]. Using Choropleth maps, [PERSON] et al. [37] evaluated eight color schemes that support different visualization tasks. [PERSON] et al. [24] performed a comparative analysis of different colormap types, with a focus on comparing the ability of single- and multi-hue color schemes to support similarity judgments. However, while numerous studies have investigated the effects of different colormaps in the context of visualization (Table 1), no studies have focused on the use of colormaps for network visualization.