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

Ross Shannon*, Aaron Quigley, Paddy Nixon

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCHI 2010 - The 28th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts
Pages4195-4200
Number of pages6
DOIs
Publication statusPublished - 9 Jun 2010
Event28th Annual CHI Conference on Human Factors in Computing Systems, CHI 2010 - Atlanta, GA, United States
Duration: 10 Apr 201015 Apr 2010

Conference

Conference28th Annual CHI Conference on Human Factors in Computing Systems, CHI 2010
Country/TerritoryUnited States
CityAtlanta, GA
Period10/04/1015/04/10

Keywords

  • Dynamic graphs
  • Graph drawing
  • Visual memory

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