  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.
