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Graphemes : self-organizing shape-based clustered structures for network visualisations
Date
2010
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.
Supervisor
Description
peer-reviewed
Publisher
Association for Computing Machinery
Citation
CHI EA '10 Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems;pp. 4195-4200
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Funding Information
Science Foundation Ireland (SFI)