Abstract
Literature data are 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 proposed a graph with rich edges to draw the temporal feature in the data. An integrated interface is developed to demonstrate the usability of our approach with case studies on IEEE Vis publications dataset.
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Acknowledgment
This material is based upon work supported by Shandong Province National Science Foundation, China (ZR2017LF006).
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Zhang, L., Jing, M., Zhou, Y. (2018). Embedded Temporal Visualization of Collaboration Networks. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_9
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DOI: https://doi.org/10.1007/978-3-030-00764-5_9
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