This folder contains 12 real-world graph data sets with background stories. A relevant study has been conducted by [Ying Zhao, Jingcheng Shi, Jiawei Liu, Jian Zhao, Fangfang Zhou, Wenzhi Zhang, Kangyi Chen, Xin Zhao, Chunyao Zhu and Wei Chen. Evaluating Effects of Background Stories on Graph Perception[J]. IEEE Transactions on Visualization and Computer Graphics, Accepted 08/2021]. In addition, the paper and supplementary materials of this study are also provided.
Each graph data set contains the following files: the .TLP format, .GML format, .SVG files, and the background story of the data set. The layout of each graph is generated by Fast Multipole Embedder algorithm in Tulip software.
A graph is an abstract model that represents relations among entities, for example, the interactions between characters in a novel. A background story endows entities and relations with real-world meanings and describes the semantics and context of the abstract model, for example, the actual story that the novel presents. Considering practical experience and prior research, human viewers who are familiar with the background story of a graph and those who do not know the background story may perceive the same graph differently. However, no previous research has adequately addressed this problem. This research paper thus presents an evaluation that investigated the effects of background stories on graph perception. Three hypotheses that focused on the role of visual focus areas, graph structure identification, and mental model formation on graph perception were formulated and guided three controlled experiments that evaluated the hypotheses using real-world graphs with background stories. An analysis of the resulting experimental data, which compared the performance of participants who read and did not read the background stories, obtained a set of instructive findings. First, having knowledge about a graph’s background story influences participants’ focus areas during interactive graph explorations. Second, such knowledge significantly affects one’s ability to identify community structures but not high degree and bridge structures. Third, this knowledge influences graph recognition under blurred visual conditions. These findings can bring new considerations to the design of storytelling visualizations and interactive graph explorations.
- Ying Zhao, Jingcheng Shi, Jiawei Liu, Jian Zhao, Fangfang Zhou, Wenzhi Zhang Kangyi Chen, Xin Zhao, Chunyao Zhu and Wei Chen. Evaluating Effects of Background Stories on Graph Perception[J]. IEEE Transactions on Visualization and Computer Graphics, Accepted 08/2021.