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TieVis: visual analytics of evolution of interpersonal ties

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Abstract

Interpersonal ties, such as strong ties and weak ties, describe the information carried by an edge in social network. Tracking the dynamic changes of interpersonal ties can thus enhance our understanding of the evolution of a complex network. Nevertheless, existing studies in dynamic network visualization mostly focus on the temporal changes of nodes or structures of the network without an adequate support of analysis and exploration of the temporal changes of interpersonal ties. In this paper, we introduce a new visual analytics method that enables interactive analysis and exploration of the dynamic changes of interpersonal ties. The method integrates four well-linked visualizations, including a scatterplot, a pixelbar chart, a layered graph, and a node–link diagram, to allow for multi-perspective analysis of the evolution of interpersonal ties. The scatterplot created by multi-dimensional scaling can help reveal the clusters of ties and detect abnormal ties, while other visualizations allow users to explore the clusters of ties interactively from different perspectives. Two case studies have been conducted to demonstrate the effectiveness of our approach.

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Notes

  1. http://cis.jhu.edu/~parky/Enron/employees.

  2. Obtained from a large online game company.

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Authors and Affiliations

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

    Fangzhou Guo, Wei Chen, Tao Lin, Biao Zhu & Yingcai Wu

  2. Zhejiang University of Technology, Hangzhou, China

    Fan Zhang

  3. Hong Kong University of Science and Technology, Hong Kong, China

    Huamin Qu

Authors
  1. Fangzhou Guo
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  2. Wei Chen
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  3. Tao Lin
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  4. Biao Zhu
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  5. Fan Zhang
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  6. Yingcai Wu
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  7. Huamin Qu
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Corresponding author

Correspondence to Wei Chen.

Additional information

Supported by Major Program of National Natural Science Foundation of China (61232012), National Natural Science Foundation of China (61422211), and National Natural Science Foundation of China (61303141), Shandong Provincial Natural Science Foundation (No. ZR2015FM022).

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Guo, F., Chen, W., Lin, T. et al. TieVis: visual analytics of evolution of interpersonal ties. J Vis 20, 905–918 (2017). https://doi.org/10.1007/s12650-017-0430-x

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  • DOI: https://doi.org/10.1007/s12650-017-0430-x

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