skip to main content
10.1145/1753846.1754125acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
extended-abstract

Graphemes: self-organizing shape-based clustered structures for network visualisations

Published: 10 April 2010 Publication History

Abstract

Network visualisations use clustering approaches to simplify the presentation of complex graph structures. We present a novel application of clustering algorithms, which controls the visual arrangement of the vertices in a cluster to explicitly encode information about that cluster. Our technique arranges parts of the graph into symbolic shapes, depending on the relative size of each cluster. Early results suggest that this layout augmentation helps viewers make sense of a graph's scale and number of elements, while facilitating recall of graph features, and increasing stability in dynamic graph scenarios.

References

[1]
Branke, J. (2001). Dynamic graph drawing. Springer Lecture Notes In Computer Science, 228--246.
[2]
Buzan, T. & Buzan, B. (2000). The mind map book. BBC.
[3]
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik 1: 269--271.
[4]
Dunbar, R. I. M. (1992). Neocortex size as a constraint on group size in primates. Journal of Human Evolution, 20, 469--493.
[5]
Eades, P. (1984). A heuristic for graph drawing. Congressus Numerantium, 42(149160), 194--202.
[6]
Eades, P. & Huang, M. L. (2000). Navigating Clustered Graphs using Force-Directed Methods. Journal of Graph Algorithms and Applications, 4(3), 157--181.
[7]
Eades, P., Lai, W., Misue, K., & Sugiyama, K. (1991). Preserving the mental map of a diagram. Proceedings of Compugraphics, 91(9), 24--33.
[8]
Girvan, M. and Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12):7821--7826.
[9]
Herman, I., Melancon, G., & Marshall, M. S. (2000). Graph Visualization and Navigation in Information Visualization: A Survey. IEEE Transactions on Visualization and Computer Graphics, 6(1), 24--43.
[10]
Miller GA (March 1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological Review 63 (2): 81--97.
[11]
Munzner, T. (2000). Interactive visualization of large graphs and networks. Ph.D. Dissertation.
[12]
O'Madadhain, J., Fisher, D., Smyth, P., White, S., & Boey, Y. (2005). Analysis and visualization of network data using JUNG. Journal of Statistical Software.
[13]
Sablowski, R. & Frick, A. (1997). Automatic graph clustering. LNCS, 395--400.
[14]
W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452--473 (1977).

Cited By

View all
  • (2021)A Comparative Study on Visualization Technique for Home NetworkProgress in Intelligent Decision Science10.1007/978-3-030-66501-2_6(71-85)Online publication date: 30-Jan-2021
  • (2013)ViStruclizer: A Structural Visualizer for Multi-dimensional Social NetworksAdvances in Knowledge Discovery and Data Mining10.1007/978-3-642-37456-2_5(49-60)Online publication date: 2013

Index Terms

  1. Graphemes: self-organizing shape-based clustered structures for network visualisations

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems
      April 2010
      2219 pages
      ISBN:9781605589305
      DOI:10.1145/1753846

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 10 April 2010

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. dynamic graphs
      2. graph drawing
      3. visual memory

      Qualifiers

      • Extended-abstract

      Conference

      CHI '10
      Sponsor:

      Acceptance Rates

      CHI EA '10 Paper Acceptance Rate 350 of 1,346 submissions, 26%;
      Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

      Upcoming Conference

      CHI 2025
      ACM CHI Conference on Human Factors in Computing Systems
      April 26 - May 1, 2025
      Yokohama , Japan

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)1
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 01 Mar 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2021)A Comparative Study on Visualization Technique for Home NetworkProgress in Intelligent Decision Science10.1007/978-3-030-66501-2_6(71-85)Online publication date: 30-Jan-2021
      • (2013)ViStruclizer: A Structural Visualizer for Multi-dimensional Social NetworksAdvances in Knowledge Discovery and Data Mining10.1007/978-3-642-37456-2_5(49-60)Online publication date: 2013

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media