Jean‐Daniel Fekete

University Name
Country flag
France

Research Interests

Explore related searches

Contact this professor

LinkedIn
ORCID
Google Scholar

Articles (10)

Six methods for transforming layered hypergraphs to apply layered graph layout algorithms

Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices—as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real‐life use cases. A copy of this paper with all appendices and supplemental material is available at osf.io/grvwu.

Year:

2022

Collaborators (5)

Paolo Buono

University of Bari Aldo Moro

ITALY

Daniel Weiskopf

University of Stuttgart

GERMANY

Dominik Moritz

Carnegie Mellon University

UNITED STATES

Sara Di Bartolomeo

University of Konstanz

GERMANY

Cody Dunne

Assistant Professor

Northeastern University

UNITED STATES
Social connections

How do I reach out?

Sign in for free to see their profile details and contact information.

Meet Kite AI