Multi-level Area Balancing of Clustered Graphs

Hsiang-Yun Wu, Martin Nöllenburg, Ivan Viola
Multi-level Area Balancing of Clustered Graphs
IEEE Transactions on Visualization and Computer Graphics (TVCG), x:1-15, December 2020. [paper] [video]

Information

Abstract

We present a multi-level area balancing technique for laying out clustered graphs to facilitate a comprehensive understanding of the complex relationships that exist in various fields, such as life sciences and sociology. Clustered graphs are often used to model relationships that are accompanied by attribute-based grouping information. Such information is essential for robust data analysis, such as for the study of biological taxonomies or educational backgrounds. Hence, the ability to smartly arrange textual labels and packing graphs within a certain screen space is therefore desired to successfully convey the attribute data . Here we propose to hierarchically partition the input screen space using Voronoi tessellations in multiple levels of detail. In our method, the position of textual labels is guided by the blending of constrained forces and the forces derived from centroidal Voronoi cells. The proposed algorithm considers three main factors: (1) area balancing, (2) schematized space partitioning, and (3) hairball management. We primarily focus on area balancing, which aims to allocate a uniform area for each textual label in the diagram. We achieve this by first untangling a general graph to a clustered graph through textual label duplication, and then coupling with spanning-tree-like visual integration. We illustrate the feasibility of our approach with examples and then evaluate our method by comparing it with well-known conventional approaches and collecting feedback from domain experts.

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BibTeX

@article{wu-2020-tvcg,
  title =      "Multi-level Area Balancing of Clustered Graphs",
  author =     "Hsiang-Yun Wu and Martin N\"{o}llenburg and Ivan Viola",
  year =       "2020",
  abstract =   "We present a multi-level area balancing technique for laying
               out clustered graphs to facilitate a comprehensive
               understanding of the complex relationships that exist in
               various fields, such as life sciences and sociology.
               Clustered graphs are often used to model relationships that
               are accompanied by attribute-based grouping information.
               Such information is essential for robust data analysis, such
               as for the study of biological taxonomies or educational
               backgrounds. Hence, the ability to smartly arrange textual
               labels and packing graphs within a certain screen space is
               therefore desired to successfully convey the attribute data
               . Here we propose to hierarchically partition the input
               screen space using Voronoi tessellations in multiple levels
               of detail. In our method, the position of textual labels is
               guided by the blending of constrained forces and the forces
               derived from centroidal Voronoi cells. The proposed
               algorithm considers three main factors: (1) area balancing,
               (2) schematized space partitioning, and (3) hairball
               management. We primarily focus on area balancing, which aims
               to allocate a uniform area for each textual label in the
               diagram. We achieve this by first untangling a general graph
               to a clustered graph through textual label duplication, and
               then coupling with spanning-tree-like visual integration. We
               illustrate the feasibility of our approach with examples and
               then evaluate our method by comparing it with well-known
               conventional approaches and collecting feedback from domain
               experts.",
  month =      dec,
  doi =        "https://doi.org/10.1109/TVCG.2020.3038154",
  journal =    "IEEE Transactions on Visualization and Computer Graphics
               (TVCG)",
  volume =     "x",
  pages =      "1--15",
  keywords =   "Graph drawing, Voronoi tessellation, multi-level,
               spatially-efficient layout",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/wu-2020-tvcg/",
}