Inspired by Biology: Towards Visualizing Complex Networks | TU Wien

Information

Publication Type: PhD-Thesis
Workgroup(s)/Project(s):
- Visualization Group
Date: 2025
Date (Start): 2021
Date (End): 2024
TU Wien Library: AC17730567
Second Supervisor: Hsiang-Yun Wu
Open Access: yes
First Supervisor: Renata Georgia Raidou
Pages: 287
Keywords: Network visualization

Abstract

The term “biological network” comprises a large and multifaceted set of different types of networks. These different network types bring with it unique visualization and visual analysis challenges. We first survey the literature in order to characterize and identify outstanding gaps in the visualization of biological networks. Inspired by these many challenges and difficulties faced by the field. Specifically, we focus on three challenges of particular interest to us: i) improving the visual quality of commonly employed straight-line node-link diagrams, ii) the visualization of uncertainty in networks, and iii) the visualization of group structures in compound graphs. To tackle these three challenges, we conduct five investigations.To tackle challenge 1, we first investigate the principled and algorithmic splitting of vertices to iteratively resolve edge crossings and thereby improve the readability of graphs. As an alternative solution to challenge 1, we investigate the visualization of so-called ego-networks, which allow for the visualization of only node-relative and node-relevant topology, instead of the entirety of a network. Third, within the context of challenge 2, we investigate the visualization of node attribute uncertainty using animated “wiggliness”, i.e., animated node motion. Fourth, in order to tackle challenge 3, we survey the current state of compound graph visualization and, finally, we combine the aforementioned four works together and develop a prototypical dashboard for the visualization of compound graphs.

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thesis

Weblinks

BibTeX

@phdthesis{Ehlers_PhD,
  title =      "Inspired by Biology: Towards Visualizing Complex Networks",
  author =     "Henry Ehlers",
  year =       "2025",
  abstract =   "The term “biological network” comprises a large and
               multifaceted set of different types of networks. These
               different network types bring with it unique visualization
               and visual analysis challenges. We first survey the
               literature in order to characterize and identify outstanding
               gaps in the visualization of biological networks. Inspired
               by these many challenges and difficulties faced by the
               field. Specifically, we focus on three challenges of
               particular interest to us: i) improving the visual quality
               of commonly employed straight-line node-link diagrams, ii)
               the visualization of uncertainty in networks, and iii) the
               visualization of group structures in compound graphs. To
               tackle these three challenges, we conduct five
               investigations.To tackle challenge 1, we first investigate
               the principled and algorithmic splitting of vertices to
               iteratively resolve edge crossings and thereby improve the
               readability of graphs. As an alternative solution to
               challenge 1, we investigate the visualization of so-called
               ego-networks, which allow for the visualization of only
               node-relative and node-relevant topology, instead of the
               entirety of a network. Third, within the context of
               challenge 2, we investigate the visualization of node
               attribute uncertainty using animated “wiggliness”, i.e.,
               animated node motion. Fourth, in order to tackle challenge
               3, we survey the current state of compound graph
               visualization and, finally, we combine the aforementioned
               four works together and develop a prototypical dashboard for
               the visualization of compound graphs.",
  pages =      "287",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  keywords =   "Network visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/Ehlers_PhD/",
}