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.
LanguageEnglish
Title of host publicationUnknown Host Publication
EditorsE Mynatt, D Schoner
Pages4195-4200
Number of pages6
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 → …

Fingerprint

Clustering algorithms
Visualization

Keywords

  • Dynamic graphs
  • graph drawing
  • visual memory.

Cite this

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 (pp. 4195-4200) https://doi.org/10.1145/1753846.1754125
Shannon, R ; Quigley, AJ ; Nixon, Patrick. / Graphemes: self-organizing shape-based clustered structures for network visualisations. Unknown Host Publication. editor / E Mynatt ; D Schoner. 2010. pp. 4195-4200
@inproceedings{930b586338564ce4a8e02fc0c9e591a5,
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.",
keywords = "Dynamic graphs, graph drawing, visual memory.",
author = "R Shannon and AJ Quigley and Patrick Nixon",
year = "2010",
doi = "10.1145/1753846.1754125",
language = "English",
isbn = "978-1-60558-930-5",
pages = "4195--4200",
editor = "E Mynatt and D Schoner",
booktitle = "Unknown Host Publication",

}

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. 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

Graphemes: self-organizing shape-based clustered structures for network visualisations. / Shannon, R; Quigley, AJ; Nixon, Patrick.

Unknown Host Publication. ed. / E Mynatt; D Schoner. 2010. p. 4195-4200.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

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.

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

ER -

Shannon R, Quigley AJ, Nixon P. Graphemes: self-organizing shape-based clustered structures for network visualisations. In Mynatt E, Schoner D, editors, Unknown Host Publication. 2010. p. 4195-4200 https://doi.org/10.1145/1753846.1754125