  Hive plots are a graph visualization style placing vertices on a set of
radial axes emanating from a common center and drawing edges as smooth curves
connecting their respective endpoints. In previous work on hive plots,
assignment to an axis and vertex positions on each axis were determined based
on selected vertex attributes and the order of axes was prespecified. Here, we
present a new framework focusing on combinatorial aspects of these drawings to
extend the original hive plot idea and optimize visual properties such as the
total edge length and the number of edge crossings in the resulting hive plots.
Our framework comprises three steps: (1) partition the vertices into multiple
groups, each corresponding to an axis of the hive plot; (2) optimize the cyclic
axis order to bring more strongly connected groups near each other; (3)
optimize the vertex ordering on each axis to minimize edge crossings. Each of
the three steps is related to a well-studied, but NP-complete computational
problem. We combine and adapt suitable algorithmic approaches, implement them
as an instantiation of our framework and show in a case study how it can be
applied in a practical setting. Furthermore, we conduct computational
experiments to gain further insights regarding algorithmic choices of the
framework. The code of the implementation and a prototype web application can
be found on OSF.

Computational Geometry;Human-Computer Interaction