  Leveraging hypergraph structures to model advanced processes has gained much
attention over the last few years in many areas, ranging from
protein-interaction in computational biology to image retrieval using machine
learning. Hypergraph models can provide a more accurate representation of the
underlying processes while reducing the overall number of links compared to
regular representations. However, interactive visualization methods for
hypergraphs and hypergraph-based models have rarely been explored or
systematically analyzed. This paper reviews the existing research landscape for
hypergraph and hypergraph model visualizations and assesses the currently
employed techniques. We provide an overview and a categorization of proposed
approaches, focusing on performance, scalability, interaction support,
successful evaluation, and the ability to represent different underlying data
structures, including a recent demand for a temporal representation of
interaction networks and their improvements beyond graph-based methods. Lastly,
we discuss the strengths and weaknesses of the approaches and give an insight
into the future challenges arising in this emerging research field.
