Six methods for transforming layered hypergraphs to apply layered graph layout algorithms
Six methods for transforming layered hypergraphs to apply layered graph layout algorithms
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变换分层超图以应用分层图布局算法的六种方法
DOI:
10.1111/cgf.14538
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发表时间:
2022
影响因子:
2.5
通讯作者:
Fekete, Jean‐Daniel
中科院分区:
文献类型:
--
作者:
Di Bartolomeo, Sara;Pister, Alexis;Buono, Paolo;Plaisant, Catherine;Dunne, Cody;Fekete, Jean‐Daniel
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.
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DOI:
--
发表时间:
2010
期刊:
Spring conference on Computer graphics
影响因子:
--
作者:
Peter Kapec
通讯作者:
Peter Kapec
DOI:
--
发表时间:
2017
期刊:
arXiv.org
影响因子:
--
作者:
Xavier Ouvrard;J. Goff;S. Marchand
通讯作者:
S. Marchand
DOI:
10.1109/tvcg.2020.3030442
发表时间:
2020-09
影响因子:
5.2
作者:
Sara Di Bartolomeo;Yixuan Zhang;Fangfang Sheng;Cody Dunne
通讯作者:
Sara Di Bartolomeo;Yixuan Zhang;Fangfang Sheng;Cody Dunne
DOI:
--
发表时间:
2004
期刊:
ACM Great Lakes Symposium on VLSI
影响因子:
--
作者:
T. Eschbach;Wolfgang Günther;B. Becker
通讯作者:
B. Becker
影响因子:
64.8
作者:
R. Thurston
通讯作者:
R. Thurston