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Network Visualization Literacy: Novel Approaches to Measurement and Instruction

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Network Science In Education

Abstract

Network visualizations, a particular kind of data visualization, can be a useful way to visually represent the relationships in real or theoretical social, physical, or biological systems. Network data can be generated and analyzed without being visualized, but the visualizations are often more compelling and may be more easily understood than numbers that summarize network properties. With the growth of network science research across a variety of domains, there is an increased call for basic literacies in networks and the ability to use network visualization as a powerful tool to understand interactions in complex systems. In this chapter, we discuss the current status of the research on network visualization literacy (NVL), how it is measured, what the current research says about NVL across a variety of contexts, ways experts are teaching to develop NVL, and recommendations based on our current understanding of best ways to improve NVL.

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Acknowledgments

This work was partially supported by the National Institutes of Health under awards P01 AG039347 and U01CA198934 and the National Science Foundation under awards NCSE 1538763, EAGER 1566393, NRT 1735095, AISL 1713567, and NCN CP Supplement 1553044. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and Affiliations

  1. Duke University, Durham, NC, USA

    Angela Zoss

  2. Indiana University, Bloomington, IN, USA

    Adam Maltese & Katy Börner

  3. New York Hall of Science, Corona, NY, USA

    Stephen Miles Uzzo

Authors
  1. Angela Zoss
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  2. Adam Maltese
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  3. Stephen Miles Uzzo
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  4. Katy Börner
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Editor information

Editors and Affiliations

  1. Data Science Institute, Columbia University, New York, NY, USA

    Catherine B. Cramer

  2. Department of Mathematics, University of California Los Angeles, Los Angeles, CA, USA

    Mason A. Porter

  3. Department of Systems Science and Industrial Engineering, Binghamton University, State University of New York, Binghamton, NY, USA

    Hiroki Sayama

  4. Center for Leadership and Diversity in STEM, Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA

    Lori Sheetz

  5. New York Hall of Science, Corona, NY, USA

    Stephen Miles Uzzo

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Zoss, A., Maltese, A., Uzzo, S.M., Börner, K. (2018). Network Visualization Literacy: Novel Approaches to Measurement and Instruction. In: Cramer, C., Porter, M., Sayama, H., Sheetz, L., Uzzo, S. (eds) Network Science In Education. Springer, Cham. https://doi.org/10.1007/978-3-319-77237-0_11

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  • DOI: https://doi.org/10.1007/978-3-319-77237-0_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77236-3

  • Online ISBN: 978-3-319-77237-0

  • eBook Packages: EducationEducation (R0)

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