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 BookConference 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.
Original languageEnglish
Title of host publicationCHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems
EditorsElizabeth Mynatt, Don Schoner, Geraldine Fitzpatrick, Scott Hudson, Keith Edwards, Tom Rodden
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Pages4195–4200
Number of pages6
ISBN (Print)9781605589305
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventCHI 2010: 28th Conference on Human Factors in Computing Systems - Atlanta, Georgia, United States
Duration: 10 Apr 201015 Apr 2010
http://www.chi2010.org/

Conference

ConferenceCHI 2010
Abbreviated titleCHI 2010
CountryUnited States
CityGeorgia
Period10/04/1015/04/10
Internet address

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    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