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Bundling-Aware Graph Drawing

Authors Daniel Archambault , Giuseppe Liotta , Martin Nöllenburg , Tommaso Piselli , Alessandra Tappini , Markus Wallinger

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LIPIcs.GD.2024.15.pdf
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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
  • Human-centered computing → Graph drawings
Keywords
  • Edge Bundling
  • Experimental Comparison
  • Graph Sparsification

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Abstract

Edge bundling algorithms significantly improve the visualization of dense graphs by reducing the clutter of many edges visible on screen by bundling them together. As such, bundling is often viewed as a post-processing step applied to a drawing, and the vast majority of edge bundling algorithms consider a graph and its drawing as input. Another way of thinking about edge bundling is to simultaneously optimize both the drawing and the bundling. In this paper, we investigate methods to simultaneously optimize a graph drawing and its bundling. We describe an algorithmic framework which consists of three main steps, namely Filter, Draw, and Bundle. We then propose two alternative implementations and experimentally compare them against the state-of-the-art approach and the simple idea of drawing and subsequently bundling the graph. The experiments confirm that bundled drawings created by our framework outperform previous approaches according to standard quality metrics for edge bundling.

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Daniel Archambault, Giuseppe Liotta, Martin Nöllenburg, Tommaso Piselli, Alessandra Tappini, and Markus Wallinger. Bundling-Aware Graph Drawing. In 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 320, pp. 15:1-15:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.GD.2024.15

Author Details

Daniel Archambault
  • Newcastle University, UK
Giuseppe Liotta
  • University of Perugia, Italy
Martin Nöllenburg
  • TU Wien, Austria
Tommaso Piselli
  • University of Perugia, Italy
Alessandra Tappini
  • University of Perugia, Italy
Markus Wallinger
  • TU Munich, Germany

Funding

  • Liotta, Giuseppe: MUR PON Proj. ARS01_00540; MUR PRIN Proj. 2022TS4Y3N; MUR PRIN Proj. 2022ME9Z78.
  • Nöllenburg, Martin: Vienna Science and Technology Fund (WWTF) [10.47379/ICT19035].
  • Piselli, Tommaso: MUR PON Proj. ARS01_00540; MUR PRIN Proj. 2022TS4Y3N; MUR PRIN Proj. 2022ME9Z78.
  • Tappini, Alessandra: MUR PON Proj. ARS01_00540; MUR PRIN Proj. 2022TS4Y3N; MUR PRIN Proj. 2022ME9Z78.
  • Wallinger, Markus: Vienna Science and Technology Fund (WWTF) [10.47379/ICT19035].

Supplementary Materials

References

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