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RankBrushers: interactive analysis of temporal ranking ensembles

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Abstract

Temporal ranking ensembles indicate time-evolving multivariate rankings. Such data can be commonly found in our daily life, for example, different rankings of universities (QS, ARWU, THE, and USNews) over year and those of NBA players over season. Effective analysis and tracking of rankings allow users to gain insights into the overall ranking change over time and seek the explanation for the change. This paper introduces a novel visual analytics approach for characterizing and visualizing the uncertainty, dynamics, and differences of ranking ensemble data. A novel visual design is proposed to characterize the evolution pattern, distribution, and uncertainty of a large number of temporal ranking ensembles. The evolutionary ranking ensembles are progressively explored, tracked, and compared by means of an intuitive visualization system. Two case studies and a task-driven user study conducted on real datasets demonstrate the effectiveness and feasibility of the implemented system.

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Article Open access 28 March 2022

Gerardo Iñiguez, Carlos Pineda, … Albert-László Barabási

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Acknowledgements

This research supported by National Key Research and Development Program (2018YFB0904503) and National Natural Science Foundation of China (U1866602, 61772456, 61761136020).

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Authors and Affiliations

  1. State Key Lab of CAD and CG, Zhejiang University, Hangzhou, 310058, Zhejiang, China

    Dongming Han, Jiacheng Pan, Fangzhou Guo, Yingcai Wu, Wenting Zheng & Wei Chen

  2. Guilin University of Electronic Technology, Guilin, China

    Xiaonan Luo

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  1. Dongming Han
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  2. Jiacheng Pan
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  3. Fangzhou Guo
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  4. Xiaonan Luo
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  5. Yingcai Wu
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  6. Wenting Zheng
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  7. Wei Chen
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Correspondence to Wei Chen.

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Han, D., Pan, J., Guo, F. et al. RankBrushers: interactive analysis of temporal ranking ensembles. J Vis 22, 1241–1255 (2019). https://doi.org/10.1007/s12650-019-00598-x

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