Jankun-Kelly, T.; Dwyer, T.; Holten, D.; Hurter, C.; Nollenburg, M.; Weaver, C.; Xu, K.

Scalability considerations for multivariate graph visualization

Book chapter

Jankun-Kelly, T., Dwyer, T., Holten, D., Hurter, C., Nollenburg, M., Weaver, C. and Xu, K. 2014. Scalability considerations for multivariate graph visualization. in: Kerren, A., Purchase, H. and Ward, M. (ed.) Multivariate Network Visualization: Dagstuhl Seminar # 13201, Dagstuhl Castle, Germany, May 12-17, 2013, Revised Discussions Springer International Publishing.

Publication dates
Chapter title	Scalability considerations for multivariate graph visualization
Authors	Jankun-Kelly, T., Dwyer, T., Holten, D., Hurter, C., Nollenburg, M., Weaver, C. and Xu, K.
Abstract	Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations.
Keywords	Algorithm Analysis and Problem Complexity, Computer Applications, Data Mining and Knowledge Discovery, Discrete Mathematics in Computer Science, Information Systems and Communication Service
Book title	Multivariate Network Visualization: Dagstuhl Seminar # 13201, Dagstuhl Castle, Germany, May 12-17, 2013, Revised Discussions
Editors	Kerren, A., Purchase, H. and Ward, M.
Publisher	Springer International Publishing
Series	Lecture Notes in Computer Science
Print	Jan 2014
Publication process dates
Deposited	07 May 2015
Output status	Published
Accepted author manuscript	ch8-scalability.pdf
Copyright Statement	The final authenticated version is available online at https://doi.org/10.1007/978-3-319-06793-3_10
Digital Object Identifier (DOI)	https://doi.org/10.1007/978-3-319-06793-3_10
Language	English

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