Paper Info
Title
GraphScape: A Model for Automated Reasoning about Visualization Similarity and Sequencing.
Abstract
We present GraphScape, a directed graph model of the vi- sualization design space that supports automated reasoning about visualization similarity and sequencing. Graph nodes represent grammar-based chart specifications and edges rep- resent 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 con- tribute (1) a method for deriving transition costs via a partial ordering of edit operations and the solution of a resulting lin- ear 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 presen- tations, elaborate transition paths between visualizations, and recommend design alternatives (e.g., to improve scalability while minimizing design changes).
Year
DOI
Venue
2017
10.1145/3025453.3025866
CHI
Keywords
Field
DocType
visualization, sequence, transition, model, automated design
Automated reasoning,Data mining,Weighting,Computer science,Visualization,Directed graph,Grammar,Theoretical computer science,Chart,Partially ordered set,Scalability
Conference
Citations 
PageRank 
References 
18
0.58
16
Authors
4
Name
Order
Citations
PageRank
Younghoon Kim1404.19
Kanit Wongsuphasawat231710.95
Jessica Hullman347726.51
Jeffrey Heer45322349.19