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

Research output: A Conference proceeding or a Chapter in Book › 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	CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems
Editors	Elizabeth Mynatt, Don Schoner, Geraldine Fitzpatrick, Scott Hudson, Keith Edwards, Tom Rodden
Place of Publication	United States
Publisher	Association for Computing Machinery (ACM)
Pages	4195–4200
Number of pages	6
ISBN (Print)	9781605589305
DOIs	https://doi.org/10.1145/1753846.1754125
Publication status	Published - 2010
Externally published	Yes
Event	CHI 2010: 28th Conference on Human Factors in Computing Systems - Atlanta, Georgia, United States Duration: 10 Apr 2010 → 15 Apr 2010 http://www.chi2010.org/

Conference

Conference	CHI 2010
Abbreviated title	CHI 2010
Country/Territory	United States
City	Georgia
Period	10/04/10 → 15/04/10
Internet address	http://www.chi2010.org/

Access to Document

10.1145/1753846.1754125

Cite this

Shannon, R., Quigley, A., & Nixon, P. (2010). Graphemes: self-organizing shape-based clustered structures for network visualisations. In E. Mynatt, D. Schoner, G. Fitzpatrick, S. Hudson, K. Edwards, & T. Rodden (Eds.), CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems (pp. 4195–4200). Association for Computing Machinery (ACM). https://doi.org/10.1145/1753846.1754125

Shannon, Ross ; Quigley, Aaron ; Nixon, Paddy. / Graphemes: self-organizing shape-based clustered structures for network visualisations. CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems. editor / Elizabeth Mynatt ; Don Schoner ; Geraldine Fitzpatrick ; Scott Hudson ; Keith Edwards ; Tom Rodden. United States : Association for Computing Machinery (ACM), 2010. pp. 4195–4200

@inproceedings{034f83b423fa46f48a0c66a37a2f39a2,

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

year = "2010",

doi = "10.1145/1753846.1754125",

language = "English",

isbn = "9781605589305",

pages = "4195–4200",

editor = "Elizabeth Mynatt and Don Schoner and Geraldine Fitzpatrick and Scott Hudson and Keith Edwards and Tom Rodden",

booktitle = "CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

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

url = "http://www.chi2010.org/",

}

Shannon, R, Quigley, A & Nixon, P 2010, Graphemes: self-organizing shape-based clustered structures for network visualisations. in E Mynatt, D Schoner, G Fitzpatrick, S Hudson, K Edwards & T Rodden (eds), CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery (ACM), United States, pp. 4195–4200, CHI 2010, Georgia, Georgia, 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 EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems. ed. / Elizabeth Mynatt; Don Schoner; Geraldine Fitzpatrick; Scott Hudson; Keith Edwards; Tom Rodden. United States: Association for Computing Machinery (ACM), 2010. p. 4195–4200.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

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

AU - Shannon, Ross

AU - Quigley, Aaron

AU - Nixon, Paddy

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 - http://www.chi2010.org/

U2 - 10.1145/1753846.1754125

DO - 10.1145/1753846.1754125

M3 - Conference contribution

SN - 9781605589305

SP - 4195

EP - 4200

BT - CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems

A2 - Mynatt, Elizabeth

A2 - Schoner, Don

A2 - Fitzpatrick, Geraldine

A2 - Hudson, Scott

A2 - Edwards, Keith

A2 - Rodden, Tom

PB - Association for Computing Machinery (ACM)

CY - United States

T2 - CHI 2010

Y2 - 10 April 2010 through 15 April 2010

ER -

Shannon R, Quigley A, Nixon P. Graphemes: self-organizing shape-based clustered structures for network visualisations. In Mynatt E, Schoner D, Fitzpatrick G, Hudson S, Edwards K, Rodden T, editors, CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems. United States: Association for Computing Machinery (ACM). 2010. p. 4195–4200 doi: 10.1145/1753846.1754125

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

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this