Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/100928
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Title: GazeGraphVis : visual analytics of gaze behaviors at multiple graph levels for path tracing tasks
Authors: Yang, Z
Xie, Y
Li, M 
Huang, GQ 
Issue Date: Aug-2023
Source: Advanced engineering informatics, Aug. 2023, v. 57, 102111
Abstract: Graph visualization contributes to an efficient understanding of interconnected properties in graph data. However, the exponential growth of interconnections poses great challenges to the efficient visual cognition of graph data. Generation of expressive graph visualization requires investigations of the cognitive process of exploring graph visualization, which can be revealed through the analysis of gaze behaviors. In this paper, we propose GazeGraphVis, a visual analytics system, to analyze gaze behaviors for path tracing tasks. Specifically, GazeGraphVis visualizes gaze behaviors at multiple levels of graph, node and edge with overview + detail techniques to provide a comprehensive analysis of human cognitive processes when finishing path tracing tasks. The insights of gaze behaviors for path tracing tasks are revealed using an integrated multiple-view interface. Domain experts in visualization and eye tracking analysis gave high praise to GazeGraphVis for its capability of obtaining the overall search tendencies and deeply analyzing the factors that affect gaze behaviors.
Keywords: Visual analytics
Cognitive process
Gaze behaviors
Graph visualization
Publisher: Elsevier
Journal: Advanced engineering informatics 
EISSN: 1474-0346
DOI: 10.1016/j.aei.2023.102111
Rights: © 2023 Elsevier Ltd. All rights reserved.
© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
The following publication Yang, Z., Xie, Y., Li, M., & Huang, G. Q. (2023). GazeGraphVis: Visual analytics of gaze behaviors at multiple graph levels for path tracing tasks. Advanced Engineering Informatics, 57, 102111 is available at https://doi.org/10.1016/j.aei.2023.102111.
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