Can animation support the visualisation of dynamic graphs?

Archambault, Daniel; Purchase, Helen C.

doi:10.1016/j.ins.2015.04.017

Cited by 55 publications

(59 citation statements)

References 50 publications

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“…A range of works [BVKW11,HIvWF11] are trying to mitigate this problem and present their superiority over baseline methods in controlled user experiments. A range of other challenges, including crossrepresentation comparisons, arises when studying representations of graphs that change over time [AP16].…”

Section: Summary Of Approachesmentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

116

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The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

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Section: Summary Of Approachesmentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

116

View full text Add to dashboard Cite

show abstract

“…With a similar experiment design, Archambault et al [APP11a] also find generally quicker response times for the timeline conditions; but for some tasks related to the appearance of entities, animation produces lower error rates. Specifically focusing on node-link layouts with a low drawing stability, Archambault and Purchase [AP15], however, report that an animated approach, better than a timeline-based small multiples approach, supports the tracing of nodes and paths over time steps. In a qualitative study, Boyandin et al [BBL12] further show that animation tends to reveal more findings on adjacent time steps, while small multiples foster the discovery of patterns lasting over longer periods.…”

Section: Animation Versus Timelinementioning

confidence: 99%

A Taxonomy and Survey of Dynamic Graph Visualization

Beck

Burch

Diehl

et al. 2016

Computer Graphics Forum

283

249

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Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. A bibliographic analysis provides insights into the organization and developmentof the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field.

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“…One such work by Archambault and Purchase [8] summarises empirical results that relate to mental map preservation in dynamic graph drawing. In a later work [9], based on the results of new studies, they review the conditions where animation and small multiples are effective and present new results for diagrams of low drawing stability. These papers focus on dynamic network visualisation and do not consider network visualisation in general.…”

Section: Related Workmentioning

confidence: 99%

Exploring the limits of complexity: A survey of empirical studies on graph visualisation

et al. 2018

Self Cite

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For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being 'large' or 'complex', yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes 'large' (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on humancentred experiments to understand how, in practice, different features and characteristics of node-link diagrams affect visual complexity.

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Can animation support the visualisation of dynamic graphs?

Cited by 55 publications

References 50 publications

Quality Metrics for Information Visualization

Quality Metrics for Information Visualization

A Taxonomy and Survey of Dynamic Graph Visualization

Exploring the limits of complexity: A survey of empirical studies on graph visualisation

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