CHORDination: Evaluating Visual Design Choices in Chord Diagrams for Network Data
<|reference_start|>CHORDination: Evaluating Visual Design Choices in Chord Diagrams for Network Data: Chord diagrams are widely used for visualizing data connectivity and flow between nodes in a network. They are effective for representing complex structures through an intuitive and visually appealing circular layout. While previous work has focused on improving aesthetics and interactivity, the influence of fundamental design elements on user perception and information retrieval remains under-explored. In this study, we explored the three primary components of chord diagram anatomy, namely the nodes, circular outline, and arc connections, in three sequential experiment phases. In phase one, we conducted a controlled experiment (N=90) to find the perceptually and information optimized node widths (narrow, medium, wide) and quantities (low, medium, high). This optimal set of node width and quantity sets the foundation for subsequent evaluations and were kept fixed for consistency. In phase two of the study, we conducted an expert design review for identifying the optimal radial tick marks and color gradients. Then in phase three, we evaluated the perceptual and information retrieval performance of the design choices in a controlled experiment (N=24) by comparing four chord diagram designs (baseline, radial tick marks, arc color gradients, both tick marks and color gradients). Results indicated that node width and quantity significantly affected users' information retrieval performance and subjective ratings, whereas the presence of tick marks predominantly influenced subjective experiences. Based on these findings, we discuss the design implications of these visual elements and offer guidance and recommendations for optimizing chord diagram designs in network visualization tasks.<|reference_end|>
@article{wang2024chordination:,
    title={CHORDination: Evaluating Visual Design Choices in Chord Diagrams for
  Network Data},
    author={Kai Wang (1), Shuqi He (1), Wenlu Wang (1), Jinbei Yu (1), Yu Liu (1)
  and Lingyun Yu (1) ((1) Xi'an Jiaotong-Liverpool University)},
    journal={arXiv preprint arXiv:2408.02268},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02268},
    primaryClass={cs.HC}
}