Discriminability of node colors in node-link diagrams and the influence of link colors

This paper examines how link colors influence the discriminability of quan- titative node color encoding in node-link diagrams. Node colors in node-link diagrams can represent quantitative attributes, such as value, frequency, or intensity. Although links themselves do not necessarily represent values, the relational aspect must be displayed with a color. This color has the potential to influence the discriminability of the node colors. Extensive research has been conducted on quantitative encoding for scatterplots; however, there is currently no research on which color combinations between nodes and links should be employed and how the quantitative values in nodes should be encoded to ensure their discriminability. This paper addresses two cases: 1. Comparing two highlighted nodes while all other nodes are gray, and 2. Comparing two nodes in a fully colored graph. We conducted two stud- ies, in which participants were required to identify color differences between two nodes in a node-link diagram with varying link properties. In the first study, conducted online, two nodes were colored, and the surrounding link topologies and link hues were varied. The node hues were also varied, while the quantitative values were encoded through saturation. The most discrim- inable node hues were used in the second study, conducted in a laboratory setting, where all nodes encoded quantitative data. Our findings indicate that the use of complementary-colored links has the effect of enhancing the discriminability of node colors, irrespective of the underlying topology. In contrast, links with a hue that is similar to the node hues leads to a reduction in discriminability. The findings of both studies were found to be consistent. In sum, our results indicate guidelines for the use of colors to enhance the discriminability of nodes in small graphs with quantitative node encoding. We recommend using shades of blue rather than yellow to quantitatively en- code nodes. To highlight the connections between nodes, pair these colors with complementary colors for the links, rather than coloring the links the same color as the nodes. Alternatively, use neutral colors, such as gray, to support the discriminability of node colors.

Authors

Institutions

Publication Details

Journal
Visual Informatics
Published
2025-10-01
DOI
https://doi.org/10.1016/j.visinf.2025.100288
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Discriminability of node colors in node-link diagrams and the influence of link colors

Tatiana von Landesberger, Margit Pohl, Rita Borgo, Laura Pelchmann
Visual Informatics
Data Visualization and Analytics
article

Discriminability of node colors in node-link diagrams and the influence of link colors

Tatiana von Landesberger, Margit Pohl, Rita Borgo, Laura Pelchmann
article en

Abstract

This paper examines how link colors influence the discriminability of quan- titative node color encoding in node-link diagrams. Node colors in node-link diagrams can represent quantitative attributes, such as value, frequency, or intensity. Although links themselves do not necessarily represent values, the relational aspect must be displayed with a color. This color has the potential to influence the discriminability of the node colors. Extensive research has been conducted on quantitative encoding for scatterplots; however, there is currently no research on which color combinations between nodes and links should be employed and how the quantitative values in nodes should be encoded to ensure their discriminability. This paper addresses two cases: 1. Comparing two highlighted nodes while all other nodes are gray, and 2. Comparing two nodes in a fully colored graph. We conducted two stud- ies, in which participants were required to identify color differences between two nodes in a node-link diagram with varying link properties. In the first study, conducted online, two nodes were colored, and the surrounding link topologies and link hues were varied. The node hues were also varied, while the quantitative values were encoded through saturation. The most discrim- inable node hues were used in the second study, conducted in a laboratory setting, where all nodes encoded quantitative data. Our findings indicate that the use of complementary-colored links has the effect of enhancing the discriminability of node colors, irrespective of the underlying topology. In contrast, links with a hue that is similar to the node hues leads to a reduction in discriminability. The findings of both studies were found to be consistent. In sum, our results indicate guidelines for the use of colors to enhance the discriminability of nodes in small graphs with quantitative node encoding. We recommend using shades of blue rather than yellow to quantitatively en- code nodes. To highlight the connections between nodes, pair these colors with complementary colors for the links, rather than coloring the links the same color as the nodes. Alternatively, use neutral colors, such as gray, to support the discriminability of node colors.

Visual Informatics
TU Wien (AT), University of Cologne (DE), King's College London (GB)
Openalex Percentile: Top 68%
Data Visualization and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.