Abstract
Interactive visual analysis plays an important role to understand complex dataset. Literature data are most often visualized as collaboration networks to show the connection between researchers. However, the static networks barely transfer much information when the dataset including temporal variable. In this paper, we propose an embedded network visualization to display the temporal patterns hiding in the data and to avoid occlusion by intelligent filters. We research different graph style such as temporal display and direction to find the best way to present the temporal feature. Also, we demonstrate the usability of our approach with case studies on real bibliographic databases.
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Jing, M., Li, X., Hu, Y. (2018). Interactive Temporal Visualization of Collaboration Networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_70
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DOI: https://doi.org/10.1007/978-3-319-77383-4_70
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