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

Citation

Shannon, R and Quigley, AJ and Nixon, PA, Graphemes: self-organizing shape-based clustered structures for network visualisations, Proceedings of the 28th International conference on Human factors in computing systems, 10-15 April 2010, Atlanta, GA, USA, pp. 4195-4200. ISBN 978-1-60558-930-5 (2010) [Conference Extract]

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DOI: doi:10.1145/1753846.1754125

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.

Item Details

Item Type:Conference Extract
Keywords:Dynamic graphs, graph drawing, visual memory.
Research Division:Information and Computing Sciences
Research Group:Information Systems
Research Field:Computer-Human Interaction
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
Author:Quigley, AJ (Associate Professor Aaron Quigley)
Author:Nixon, PA (Professor Paddy Nixon)
ID Code:68648
Year Published:2010
Deposited By:Computing and Information Systems
Deposited On:2011-03-16
Last Modified:2011-06-02
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