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

Ross Shannon; Aaron Quigley; Paddy Nixon

doi:10.1145/1753846.1754125

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

Ross Shannon^*, Aaron Quigley, Paddy Nixon

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English
Title of host publication	CHI 2010 - The 28th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts
Pages	4195-4200
Number of pages	6
DOIs	https://doi.org/10.1145/1753846.1754125
Publication status	Published - 9 Jun 2010
Event	28th Annual CHI Conference on Human Factors in Computing Systems, CHI 2010 - Atlanta, GA, United States Duration: 10 Apr 2010 → 15 Apr 2010

Conference

Conference	28th Annual CHI Conference on Human Factors in Computing Systems, CHI 2010
Country/Territory	United States
City	Atlanta, GA
Period	10/04/10 → 15/04/10

Keywords

Dynamic graphs
Graph drawing
Visual memory

Access to Document

10.1145/1753846.1754125

Cite this

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title = "Graphemes: Self-organizing shape-based clustered structures for network visualisations",

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.",

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author = "Ross Shannon and Aaron Quigley and Paddy Nixon",

year = "2010",

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doi = "10.1145/1753846.1754125",

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pages = "4195--4200",

booktitle = "CHI 2010 - The 28th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts",

note = "28th Annual CHI Conference on Human Factors in Computing Systems, CHI 2010 ; Conference date: 10-04-2010 Through 15-04-2010",

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Shannon, R, Quigley, A & Nixon, P 2010, Graphemes: Self-organizing shape-based clustered structures for network visualisations. in CHI 2010 - The 28th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts. pp. 4195-4200, 28th Annual CHI Conference on Human Factors in Computing Systems, CHI 2010, Atlanta, GA, United States, 10/04/10. https://doi.org/10.1145/1753846.1754125

Graphemes: Self-organizing shape-based clustered structures for network visualisations. / Shannon, Ross; Quigley, Aaron; Nixon, Paddy.
CHI 2010 - The 28th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts. 2010. p. 4195-4200.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Quigley, Aaron

AU - Nixon, Paddy

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N2 - 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.

AB - 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.

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KW - Graph drawing

KW - Visual memory

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BT - CHI 2010 - The 28th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts

Y2 - 10 April 2010 through 15 April 2010

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

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