Investigating Visual Perception of Degree Centrality in Graph Visualization

作者：Xin Zhao, Shuowen Fu, Rui Yang, Lei Yang, Yunpeng Chen, Jiang Zhang, Jiang Long, Fangfang Zhou, Ying Zhao · 发表于：IEEE Transactions on Visualization and Computer Graphics · 年份：2025 · DOI：10.1109/tvcg.2025.3567129 · 被引用次数：8 · 研究领域：Data Visualization and Analytics、Graph Theory and Algorithms

Degree centrality (DC) is a widely used metric that measures node importance in data space. A node-link diagram is a commonly used graph visualization to help viewers identify important nodes in visual space. Previous graph perception studies largely concentrated on revealing perception principles in visual space. However, they rarely investigated the intrinsic relations between computed and perceived important nodes by jointly using data and visual spaces, thereby hindering a deep integration of computational and interactive graph analytics. To address this gap, we adopted the visual perception of DC as a representative object to conduct a graph perception study by jointly using data and visual spaces. Two research questions were defined. (RQ1) Can viewers accurately estimate the relative DCs of the given nodes in a node-link diagram through visual perception? (RQ2) What visual factors influence viewers' visual estimation of relative DCs? A controlled user experiment was conducted to answer the questions. Results showed that: (1) The participants failed to estimate the relative DCs accurately, particularly when the DC differences between nodes were not great. (2) Seven visual factors influencing the tasks were summarized, such as the size of the visual receptive region of a node, the link and node densities in the visual receptive region of the node, and the wrapping angle of the node's neighbors. (3) The factors presented certain priorities in complicated situations. These ...