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

R Shannon; AJ Quigley; Patrick Nixon

doi:10.1145/1753846.1754125

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

R Shannon, AJ Quigley, Patrick Nixon

Computer Science and Informatics Research

n/a

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

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 language	English
Title of host publication	Unknown Host Publication
Editors	E Mynatt, D Schoner
Publisher	Association for Computing Machinery
Pages	4195-4200
Number of pages	6
ISBN (Print)	978-1-60558-930-5
DOIs	https://doi.org/10.1145/1753846.1754125
Publication status	Published (in print/issue) - 2010
Event	Proceedings of the 28th International Conference on Human Factors in Computing Systems - Atlanta, GA, USA Duration: 1 Jan 2010 → …

Conference

Conference	Proceedings of the 28th International Conference on Human Factors in Computing Systems
Period	1/01/10 → …

Keywords

Dynamic graphs
graph drawing
visual memory.

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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{\textquoteright}s scale and number of elements, while facilitating recall of graph features, and increasing stability in dynamic graph scenarios.",

keywords = "Dynamic graphs, graph drawing, visual memory.",

author = "R Shannon and AJ Quigley and Patrick Nixon",

year = "2010",

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editor = "E Mynatt and D Schoner",

booktitle = "Unknown Host Publication",

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note = "Proceedings of the 28th International Conference on Human Factors in Computing Systems ; Conference date: 01-01-2010",

}

Shannon, R, Quigley, AJ & Nixon, P 2010, Graphemes: self-organizing shape-based clustered structures for network visualisations. in E Mynatt & D Schoner (eds), Unknown Host Publication. Association for Computing Machinery, pp. 4195-4200, Proceedings of the 28th International Conference on Human Factors in Computing Systems, 1/01/10. https://doi.org/10.1145/1753846.1754125

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T1 - Graphemes: self-organizing shape-based clustered structures for network visualisations

AU - Shannon, R

AU - Quigley, AJ

AU - Nixon, Patrick

PY - 2010

Y1 - 2010

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.

KW - Dynamic graphs

KW - graph drawing

KW - visual memory.

UR - https://www.scopus.com/pages/publications/77953088152

U2 - 10.1145/1753846.1754125

DO - 10.1145/1753846.1754125

M3 - Conference contribution

SN - 978-1-60558-930-5

SP - 4195

EP - 4200

BT - Unknown Host Publication

A2 - Mynatt, E

A2 - Schoner, D

PB - Association for Computing Machinery

T2 - Proceedings of the 28th International Conference on Human Factors in Computing Systems

Y2 - 1 January 2010

ER -

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

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