# A Study of Colormaps in Network Visualization
## 5 Discussion  
In this study, we evaluated the task completion time and correctness rate of four colormaps through a quantitative analysis. The results of the user study indicate that _blue_ (single-hue, sequential) has the  
Figure 4: Results of the experiment for the _correctness rate_. (**a**) shows the overall _correctness rate_ for each _colormap_, which is then broken down further into _datasets_ (**b**), _span_ (**c**), and _node attributes_ (**d**). shortest task completion time followed by _RdYIBu_ (diverging), _viridis_ (multi-hue, sequential), and _jet_ (rainbow), respectively. In a previous study [24], _blue_ and _viridis_ had the shortest task completion times and also showed significantly different results from the diverging and rainbow colormaps. This shows that participants completed their tasks faster when using colormaps with fewer hues. This may be because the perceptual distances across different hues are less apparent as compared to those of a colormap with only a few hues (1-3) throughout the whole colormap. Further, this discordance may occur as a result of the increased effort involved in consulting the given colormap, which eventually increased the task completion time. That is why participants require more time for task completion when configuring distances with colormaps other than blue. On the other hand, _viridis_ (multi-hue, sequential) demonstrated a higher correctness rate than the other colormaps. A previous study [24] also found _viridis_ to be superior to other colormaps for correctness rate. In addition, with a small span value, the performance of the _blue_ colormap is degraded. Although _blue_ colormaps performed well with respect to task completion time, they were found to be the most error-prone, below all of the other colormaps. Overall, participants performed the tasks more accurately when using the higher span value of _40_. As the span value decreased and the comparison nodes became closer to each other, the accuracy dropped. As a result of the single hue in the _blue_ colormap, when the span value was small and the comparison nodes were closer to each other, it was very difficult for participants to achieve a good level of accuracy. On the other hand, regarding the other colormaps, even when the comparison nodes were close to each other, participants were still able to differentiate between them, as they contained more than one hue. Another important reason for _blue_ performing badly as compared to the other colormaps could be the high luminance of the colormap. With a white background, the comparison/reference node with a high luminance value makes it more difficult for the participants to accomplish the task accurately, which was not the case for the other colormaps. Structural and non-structural node attributes did not show a significant difference in terms of task completion time. However, there was a significant difference in the correctness rate. Thus, the effect of a given colormap can differ depending on the node attribute in network visualization. We suspect that this difference is caused by the spatial proximity between nodes. The positions of nodes were computed with a layout algorithm [42], where structurally similar nodes were close to each other. Thus, it is possible that the spatial node position plays an effective role in the difference between the correctness rates of structural and non-structural node attributes. A previous study [24] evaluated colormaps in isolation and found that _viridis_ and _blue_ outperformed the other colormaps. However, this was not the case in our study, which focused on the evaluation of network visualization. This shows that colormaps do not always perform the same way for different kinds of visualization techniques. Thus, visualization designers should consider the importance of colormaps in network visualization as well as their performance when encoding data.