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GroupView: A Visual Framework for Exploring Group Membership Dynamics over Time

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  • Mithilesh Kumar Singh

    (Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA)

  • Klaus Mueller

    (Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA)

Abstract

Tracking group membership dynamics over time is a persistent challenge in visual analytics, particularly when dealing with complex, multidimensional datasets. Existing tools often struggle to visualize dynamic group transitions while preserving attribute relationships and maintaining consistent group definitions. We present GroupView, a visual framework designed to explore temporal data and group dynamics to address this. GroupView enables users to slice data into time-based segments and create dynamic groupings, facilitating the identification of trends and patterns that may otherwise remain hidden. Its features include automated grouping based on data similarities, combinatorial grouping for richer insights, and custom grouping for tailored analysis. A heuristic user study involving visualization experts provided feedback on usability and analytical value, highlighting the strengths of GroupView in intuitive exploration and insight discovery. These features position GroupView as a valuable tool for analysts and researchers working with evolving datasets, offering new avenues for uncovering trends and tracking group-level changes over time.

Suggested Citation

  • Mithilesh Kumar Singh & Klaus Mueller, 2025. "GroupView: A Visual Framework for Exploring Group Membership Dynamics over Time," Data, MDPI, vol. 10(8), pages 1-29, August.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:8:p:133-:d:1729182
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