Me! Me! Me! Me! A study and comparison of ego network representations | TU Wien

Information

Publication Type: Journal Paper (without talk)
Workgroup(s)/Project(s):
- Visualization Group
Date: December 2024
Article Number: 104123
DOI: 10.1016/j.cag.2024.104123
ISSN: 1873-7684
Journal: COMPUTERS & GRAPHICS-UK
Open Access: yes
Pages: 15
Volume: 125
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Pages: 1 – 15
Keywords: Adjacency matrix, Ego network visualization, Layered node-link diagram, Radial node-link diagram, Straight-line node-link diagram, User study

Abstract

From social networks to brain connectivity, ego networks are a simple yet powerful approach to visualizing parts of a larger graph, i.e. those related to a selected focal node — the so-called “ego”. While surveys and comparisons of general graph visualization approaches exist in the literature, we note (i) the many conflicting results of comparisons of adjacency matrices and node-link diagrams, thus motivating further study, as well as (ii) the absence of such systematic comparisons for ego networks specifically. In this paper, we propose the development of empirical recommendations for ego network visualization strategies. First, we survey the literature across application domains and collect examples of network visualizations to identify the most common visual encodings, namely straight-line, radial, and layered node-link diagrams, as well as adjacency matrices. These representations are then applied to a representative, intermediate-sized network and subsequently compared in a large-scale, crowd-sourced user study in a mixed-methods analysis setup to investigate their impact on both user experience and performance. Within the limits of this study, and contrary to previous comparative investigations of adjacency matrices and node-link diagrams (outside of ego networks specifically), participants performed systematically worse when using adjacency matrices than those using node-link diagrammatic representations. Similar to previous comparisons of different node-link diagrams, we do not detect any notable differences in participant performance between the three node-link diagrams. Lastly, our quantitative and qualitative results indicate that participants found adjacency matrices harder to learn, use, and understand than node-link diagrams. We conclude that in terms of both participant experience and performance, a layered node-link diagrammatic representation appears to be the most preferable for ego network visualization purposes.

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BibTeX

@article{ehlers-2024-mmm,
  title =      "Me! Me! Me! Me! A study and comparison of ego network
               representations",
  author =     "Henry Ehlers and Daniel Pahr and Velitchko Filipov and
               Hsiang-Yun Wu and Renata Raidou",
  year =       "2024",
  abstract =   "From social networks to brain connectivity, ego networks are
               a simple yet powerful approach to visualizing parts of a
               larger graph, i.e. those related to a selected focal node
               — the so-called “ego”. While surveys and comparisons
               of general graph visualization approaches exist in the
               literature, we note (i) the many conflicting results of
               comparisons of adjacency matrices and node-link diagrams,
               thus motivating further study, as well as (ii) the absence
               of such systematic comparisons for ego networks
               specifically. In this paper, we propose the development of
               empirical recommendations for ego network visualization
               strategies. First, we survey the literature across
               application domains and collect examples of network
               visualizations to identify the most common visual encodings,
               namely straight-line, radial, and layered node-link
               diagrams, as well as adjacency matrices. These
               representations are then applied to a representative,
               intermediate-sized network and subsequently compared in a
               large-scale, crowd-sourced user study in a mixed-methods
               analysis setup to investigate their impact on both user
               experience and performance. Within the limits of this study,
               and contrary to previous comparative investigations of
               adjacency matrices and node-link diagrams (outside of ego
               networks specifically), participants performed
               systematically worse when using adjacency matrices than
               those using node-link diagrammatic representations. Similar
               to previous comparisons of different node-link diagrams, we
               do not detect any notable differences in participant
               performance between the three node-link diagrams. Lastly,
               our quantitative and qualitative results indicate that
               participants found adjacency matrices harder to learn, use,
               and understand than node-link diagrams. We conclude that in
               terms of both participant experience and performance, a
               layered node-link diagrammatic representation appears to be
               the most preferable for ego network visualization purposes.",
  month =      dec,
  articleno =  "104123",
  doi =        "10.1016/j.cag.2024.104123",
  issn =       "1873-7684",
  journal =    "COMPUTERS & GRAPHICS-UK",
  pages =      "15",
  volume =     "125",
  publisher =  "PERGAMON-ELSEVIER SCIENCE LTD",
  pages =      "1--15",
  keywords =   "Adjacency matrix, Ego network visualization, Layered
               node-link diagram, Radial node-link diagram, Straight-line
               node-link diagram, User study",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-mmm/",
}