Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs

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Botong Qu, Eugene Zhang, Yue Zhang

 External link (DOI) 

 View presentation:2021-10-29T16:15:00ZGMT-0600Change your timezone on the schedule page
2021-10-29T16:15:00Z

Exemplar figure, described by caption below — We provide an automatic optimization framework for high-quality polygon layout of hypergraph data, such as the author-paper data shown in this Figure. The top is a paper-centric view and the bottom is an author-centric view. The primal-dual views were generated using our joint optimization framework.

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Abstract

N-ary relationships, which relate N entities where N is not necessarily two, can be visually represented as polygons whose vertices are the entities of the relationships. Manually generating a high-quality layout using this representation is labor-intensive. In this paper, we provide an automatic polygon layout generation algorithm for the visualization of N-ary relationships. At the core of our algorithm is a set of objective functions motivated by a number of design principles that we have identified. These objective functions are then used in an optimization framework that we develop to achieve high-quality layouts. Recognizing the duality between entities and relationships in the data, we provide a second visualization in which the roles of entities and relationships in the original data are reversed. This can lead to additional insight about the data. Furthermore, we enhance our framework for a joint optimization on the primal layout (original data) and the dual layout (where the roles of entities and relationships are reversed). This allows users to inspect their data using two complementary views. We apply our visualization approach to a number of datasets that include co-authorship data and social contact pattern data.