A User Study on Multivariate Edge Visualizations for Graph-Based Visual Analysis Tasks

Schöffel, Sebastian; Schwank, Johannes W.; Ebert, Achim

doi:10.1109/iv.2016.41

Cited by 10 publications

(13 citation statements)

References 9 publications

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“…Controlled experiments were also common and were most often conducted in one of two scenarios: (1) comparing a novel MVN approach with existing techniques [LPB * 06, JRA09, ARRC11, YDGM17]; or (2) comparing variations of a given design for visualizing MVNs [SSE16,BW11].…”

Section: Discussionmentioning

confidence: 99%

The State of the Art in Visualizing Multivariate Networks

Nobre

Meyer

Streit

et al. 2019

Computer Graphics Forum

111

112

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Multivariate networks are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Most real‐world networks are associated with several attributes, and many analysis tasks depend on analyzing both, relationships and attributes. Visualization of multivariate networks, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this state‐of‐the‐art report, we analyze current practices and classify techniques along four axes: layouts, view operations, layout operations, and data operations. We also provide an analysis of tasks specific to multivariate networks and give recommendations for which technique to use in which scenario. Finally, we survey application areas and evaluation methodologies.

show abstract

Section: Discussionmentioning

confidence: 99%

The State of the Art in Visualizing Multivariate Networks

Nobre

Meyer

Streit

et al. 2019

Computer Graphics Forum

111

112

View full text Add to dashboard Cite

show abstract

“…With more complex, real-world datasets, use cases provide stronger evidence that the proposed technique could be effective in the wild. Controlled experiments were also common and were most often conducted in one of two scenarios: (1) comparing a novel MVN approach with existing techniques [LPB * 06, JRA09, ARRC11, YDGM17]; or (2) comparing variations of a given design for visualizing MVNs [SSE16,BW11].…”

Section: Discussionmentioning

confidence: 99%

The State of the Art in Visualizing Multivariate Networks

Nobre¹,

Meyer²,

Streit³

et al. 2019

Preprint

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Multivariate networks are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Most real-world networks are associated with several attributes, and many analysis tasks depend on analyzing both, relationships and attributes. Visualization of multivariate networks, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this state-of-the-art report, we analyze current practices and classify techniques along four axes: layouts, view operations, layout operations, and data operations. We also provide an analysis of tasks specific to multivariate networks and give recommendations for which technique to use in which scenario. Finally, we survey application areas and evaluation methodologies.

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“…A follow-up study [138] with 18 participants showed that even other edge variants based on shapes or geometry have some benefits in AR. Summary: There seems to be a tendency to simpler representations if many edge properties are contained in a graph dataset, for example, as bar charts [136]. An influence of edge encoding styles [133] was detected.…”

Section: B: Edge Propertiesmentioning

confidence: 94%

“…An approach to displaying multivariate data on graph edges was studied by Schöffel et al [136]. Bar charts of various types were displayed on the edges between nodes.…”

Section: B: Edge Propertiesmentioning

confidence: 99%

The State of the Art in Empirical User Evaluation of Graph Visualizations

et al. 2021

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While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this article, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation, not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions.

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A User Study on Multivariate Edge Visualizations for Graph-Based Visual Analysis Tasks

Cited by 10 publications

References 9 publications

The State of the Art in Visualizing Multivariate Networks

The State of the Art in Visualizing Multivariate Networks

The State of the Art in Visualizing Multivariate Networks

The State of the Art in Empirical User Evaluation of Graph Visualizations

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