Bayesian Modelling of Alluvial Diagram Complexity

Anjana Arunkumar, Shashank Ginjpalli, Chris Bryan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Alluvial diagrams are a popular technique for visualizing flow and relational data. However, successfully reading and interpreting the data shown in an alluvial diagram is likely influenced by factors such as data volume, complexity, and chart layout. To understand how alluvial diagram consumption is impacted by its visual features, we conduct two crowdsourced user studies with a set of alluvial diagrams of varying complexity, and examine (i) participant performance on analysis tasks, and (ii) the perceived complexity of the charts. Using the study results, we employ Bayesian modelling to predict participant classification of diagram complexity. We find that, while multiple visual features are important in contributing to alluvial diagram complexity, interestingly the importance of features seems to depend on the type of complexity being modeled, i.e. task complexity vs. perceived complexity.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781665433358
StatePublished - 2021
Event2021 IEEE Visualization Conference, VIS 2021 - Virtual, Online, United States
Duration: Oct 24 2021Oct 29 2021

Publication series

NameProceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021


Conference2021 IEEE Visualization Conference, VIS 2021
Country/TerritoryUnited States
CityVirtual, Online


  • Empirical studies in visualization
  • Human-centered computing
  • Visualization
  • Visualization techniques

ASJC Scopus subject areas

  • Computer Science Applications
  • Media Technology
  • Modeling and Simulation


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