Network visualization retargeting

Giacomo, Emilio Di; Didimo, Walter; Liotta, Giuseppe; Montecchiani, Fabrizio

doi:10.1109/iisa.2015.7388095

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…To enhance scalability, prior work contributes algorithms for managing levels of detail through progressive refinement [RH09,RZH12] or by limiting "the number of visual entities" [EF10]). Visualization retargeting studies (e.g., Wu et al [WLLM13], Di Giacomo et al [DDLM15]) provide algorithms for resizing charts while keeping visually salient information. Scalability concerns arise across various device types, such as scaling up desktop visualizations to wall-sized displays [RJH11,JH13] and non-rectangular devices (e.g., circular tabletops [VLS02], smart watches [BBB * 19]).…”

Section: Needs For Responsive Visualizationmentioning

confidence: 99%

Design Patterns and Trade‐Offs in Responsive Visualization for Communication

Kim¹,

Moritz

Hullman³

2021

Computer Graphics Forum

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Increased access to mobile devices motivates the need to design communicative visualizations that are responsive to varying screen sizes. However, relatively little design guidance or tooling is currently available to authors. We contribute a detailed characterization of responsive visualization strategies in communication‐oriented visualizations, identifying 76 total strategies by analyzing 378 pairs of large screen (LS) and small screen (SS) visualizations from online articles and reports. Our analysis distinguishes between the Targets of responsive visualization, referring to what elements of a design are changed and Actions representing how targets are changed. We identify key trade‐offs related to authors' need to maintain graphical density, referring to the amount of information per pixel, while also maintaining the “message” or intended takeaways for users of a visualization. We discuss implications of our findings for future visualization tool design to support responsive transformation of visualization designs, including requirements for automated recommenders for communication‐oriented responsive visualizations.

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Section: Needs For Responsive Visualizationmentioning

confidence: 99%

Design Patterns and Trade‐Offs in Responsive Visualization for Communication

Kim¹,

Moritz

Hullman³

2021

Computer Graphics Forum

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“…Analyzing 378 responsive visualization pairs on desktop and mobile devices, Kim et al [43] identify density-message trade-offs in responsive visualization where authors need to balance adjusting visual density or complexity for different screen types while maintaining patterns, trends or other important information conveyed in the source view. Focusing on maintaining key information at different scales, earlier work on visualization resizing introduces algorithms that repeatedly remove the pixels determined to be least important [18] and iteratively minimize scaling in more salient regions [76], for example. We extend prior approaches by proposing approximation methods for task-oriented visualization insights.…”

Section: Responsive Visualization and Design Transformationsmentioning

confidence: 99%

An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization

Kim¹,

Rossi

Sarma³

et al. 2021

Preprint

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Authors often transform a large screen visualization for smaller displays through rescaling, aggregation and other techniques when creating visualizations for both desktop and mobile devices (i.e., responsive visualization). However, transformations can alter relationships or patterns implied by the large screen view, requiring authors to reason carefully about what information to preserve while adjusting their design for the smaller display. We propose an automated approach to approximating the loss of support for task-oriented visualization insights (identification, comparison, and trend) in responsive transformation of a source visualization. We operationalize identification, comparison, and trend loss as objective functions calculated by comparing properties of the rendered source visualization to each realized target (small screen) visualization. To evaluate the utility of our approach, we train machine learning models on human ranked small screen alternative visualizations across a set of source visualizations. We find that our approach achieves an accuracy of 84% (random forest model) in ranking visualizations. We demonstrate this approach in a prototype responsive visualization recommender that enumerates responsive transformations using Answer Set Programming and evaluates the preservation of task-oriented insights using our loss measures. We discuss implications of our approach for the development of automated and semi-automated responsive visualization recommendation.

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