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

R Shannon, AJ Quigley, Patrick Nixon

    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 publicationUnknown Host Publication
    EditorsE Mynatt, D Schoner
    PublisherAssociation for Computing Machinery
    Pages4195-4200
    Number of pages6
    ISBN (Print)978-1-60558-930-5
    DOIs
    Publication statusPublished - 2010
    EventProceedings of the 28th International Conference on Human Factors in Computing Systems - Atlanta, GA, USA
    Duration: 1 Jan 2010 → …

    Conference

    ConferenceProceedings of the 28th International Conference on Human Factors in Computing Systems
    Period1/01/10 → …

    Keywords

    • Dynamic graphs
    • graph drawing
    • visual memory.

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