GraphScape: A model for automated reasoning about visualization similarity and sequencing

Younghoon Kim; Kanit Wongsuphasawat; Jessica Hullman; Jeffrey Heer

doi:10.1145/3025453.3025866

GraphScape: A model for automated reasoning about visualization similarity and sequencing

Younghoon Kim, Kanit Wongsuphasawat, Jessica Hullman, Jeffrey Heer

Computer Science

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

81 Scopus citations

Abstract

We present GraphScape, a directed graph model of the visualization design space that supports automated reasoning about visualization similarity and sequencing. Graph nodes represent grammar-based chart specifications and edges represent edits that transform one chart to another. We weight edges with an estimated cost of the difficulty of interpreting a target visualization given a source visualization. We contribute (1) a method for deriving transition costs via a partial ordering of edit operations and the solution of a resulting linear program, and (2) a global weighting term that rewards consistency across transition subsequences. In a controlled experiment, subjects rated visualization sequences covering a taxonomy of common transition types. In all but one case, GraphScape's highest-ranked suggestion aligns with subjects' top-rated sequences. Finally, we demonstrate applications of GraphScape to automatically sequence visualization presentations, elaborate transition paths between visualizations, and recommend design alternatives (e.g., to improve scalability while minimizing design changes).

Original language	English (US)
Title of host publication	CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publication	Explore, Innovate, Inspire
Publisher	Association for Computing Machinery
Pages	2628-2638
Number of pages	11
ISBN (Electronic)	9781450346559
DOIs	https://doi.org/10.1145/3025453.3025866
State	Published - May 2 2017
Event	2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States Duration: May 6 2017 → May 11 2017

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings
Volume	2017-May

Other

Other	2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Country/Territory	United States
City	Denver
Period	5/6/17 → 5/11/17

Funding

This work was supported by the Paul G. Allen Family Foundation, a Moore Foundation Data-Driven Discovery Investigator Award, and DARPA XDATA.

Keywords

Automated design
Model
Sequence
Transition
Visualization

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Graphics and Computer-Aided Design

Access to Document

10.1145/3025453.3025866

Cite this

Kim, Y., Wongsuphasawat, K., Hullman, J., & Heer, J. (2017). GraphScape: A model for automated reasoning about visualization similarity and sequencing. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire (pp. 2628-2638). (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2017-May). Association for Computing Machinery. https://doi.org/10.1145/3025453.3025866

Kim, Younghoon ; Wongsuphasawat, Kanit ; Hullman, Jessica et al. / GraphScape : A model for automated reasoning about visualization similarity and sequencing. CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Association for Computing Machinery, 2017. pp. 2628-2638 (Conference on Human Factors in Computing Systems - Proceedings).

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abstract = "We present GraphScape, a directed graph model of the visualization design space that supports automated reasoning about visualization similarity and sequencing. Graph nodes represent grammar-based chart specifications and edges represent edits that transform one chart to another. We weight edges with an estimated cost of the difficulty of interpreting a target visualization given a source visualization. We contribute (1) a method for deriving transition costs via a partial ordering of edit operations and the solution of a resulting linear program, and (2) a global weighting term that rewards consistency across transition subsequences. In a controlled experiment, subjects rated visualization sequences covering a taxonomy of common transition types. In all but one case, GraphScape's highest-ranked suggestion aligns with subjects' top-rated sequences. Finally, we demonstrate applications of GraphScape to automatically sequence visualization presentations, elaborate transition paths between visualizations, and recommend design alternatives (e.g., to improve scalability while minimizing design changes).",

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author = "Younghoon Kim and Kanit Wongsuphasawat and Jessica Hullman and Jeffrey Heer",

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Kim, Y, Wongsuphasawat, K, Hullman, J & Heer, J 2017, GraphScape: A model for automated reasoning about visualization similarity and sequencing. in CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Conference on Human Factors in Computing Systems - Proceedings, vol. 2017-May, Association for Computing Machinery, pp. 2628-2638, 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017, Denver, United States, 5/6/17. https://doi.org/10.1145/3025453.3025866

GraphScape: A model for automated reasoning about visualization similarity and sequencing. / Kim, Younghoon; Wongsuphasawat, Kanit; Hullman, Jessica et al.
CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Association for Computing Machinery, 2017. p. 2628-2638 (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2017-May).

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

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Kim Y, Wongsuphasawat K, Hullman J, Heer J. GraphScape: A model for automated reasoning about visualization similarity and sequencing. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Association for Computing Machinery. 2017. p. 2628-2638. (Conference on Human Factors in Computing Systems - Proceedings). doi: 10.1145/3025453.3025866

GraphScape: A model for automated reasoning about visualization similarity and sequencing

Abstract

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Keywords

ASJC Scopus subject areas

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