Visual Comparison of Networks in VR

Joos, Lucas; Jaeger-Honz, Sabrina; Schreiber, Falk; Keim, Daniel A.; Klein, Karsten

doi:10.1109/tvcg.2022.3203001

Cited by 14 publications

(13 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, McGee et al [MGM*19] state in their literature survey the importance of these cues, as 3D graph visualizations without stereoscopic/motion cues offer no benefits over 2D. Other studies [FPK*23, JJHS*22] have looked towards using Virtual Reality ( VR ) to explore the differences between 2D, 2.5D, and 3D network visualizations and found that different tasks were done more effectively using different number of dimensions. Research in other fields show mixed results in the comparison of 2D versus 3D.…”

Section: Related Workmentioning

confidence: 99%

An Experimental Evaluation of Viewpoint‐Based 3D Graph Drawing

van Wageningen,

Mchedlidze,

Telea

2024

Computer Graphics Forum

View full text Add to dashboard Cite

Node‐link diagrams are a widely used metaphor for creating visualizations of relational data. Most frequently, such techniques address creating 2D graph drawings, which are easy to use on computer screens and in print. In contrast, 3D node‐link graph visualizations are far less used, as they have many known limitations and comparatively few well‐understood advantages. A key issue here is that such 3D visualizations require users to select suitable viewpoints. We address this limitation by studying the ability of layout techniques to produce high‐quality views of 3D graph drawings. For this, we perform a thorough experimental evaluation, comparing 3D graph drawings, rendered from a covering sampling of all viewpoints, with their 2D counterparts across various state‐of‐the‐art node‐link drawing algorithms, graph families, and quality metrics. Our results show that, depending on the graph family, 3D node‐link diagrams can contain a many viewpoints that yield 2D visualizations that are of higher quality than those created by directly using 2D node‐link diagrams. This not only sheds light on the potential of 3D node‐link diagrams but also gives a simple approach to produce high‐quality 2D node‐link diagrams.

show abstract

Section: Related Workmentioning

confidence: 99%

An Experimental Evaluation of Viewpoint‐Based 3D Graph Drawing

van Wageningen,

Mchedlidze,

Telea

2024

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…While many of these can be generalised to 3D (Herman et al, 2000), work on graph layouts specifically developed for 3D is comparatively sparse (Hong and Murtagh, 2005). There are, however, observations that 3D graph visualisations can outperform established 2D visualisations such as adjacency matrices, for instance in network comparison tasks (Joos et al, 2022). It is also reported that 3D layouts can mitigate problems with edge crossings or node overlaps that are frequently found in complex 2D layouts (Kotlarek et al, 2020).…”

Section: Graph Visualisation In 3dmentioning

confidence: 99%

“…This preserves key characteristics of the 2D graph and ensures a good overview over the entire network also in 3D (Kwon et al, 2016) and should therefore help users to maintain a connection to the graph on the 2D display. Although there are more "organic" 3D node-link layouts utilising more extensively immersive space as for instance presented by Kotlarek et al (2020), these layouts perform not necessarily better than 2D layouts (Joos et al, 2022). Some studies indicate that spherical layouts in VR can improve error rates and completion times for a variety of typical graph analysis tasks, such as finding common neighbours, highest degree nodes and paths, as well as recalling node locations (Kwon et al, 2015).…”

Section: Data Set and Layout Algorithmmentioning

confidence: 99%

“…Some studies indicate that spherical layouts in VR can improve error rates and completion times for a variety of typical graph analysis tasks, such as finding common neighbours, highest degree nodes and paths, as well as recalling node locations (Kwon et al, 2015). Furthermore, spherical layouts can reduce frequently mentioned problems with occlusion (Jeong and Pang, 1998;Joos et al, 2022) and edge crossings (Archambault et al, 2007), particularly in 3D (Lu and Si, 2020).…”

Section: Data Set and Layout Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Transforming graph data visualisations from 2D displays into augmented reality 3D space: A quantitative study

Schwajda

Friedl

Pointecker

et al. 2023

Front. Virtual Real.

View full text Add to dashboard Cite

Modern video-based head-mounted displays allow users to operate along Milgram’s entire reality-virtuality continuum. This opens up the field for novel cross-reality applications that distribute data analytics tasks along this continuum to combine benefits of established 2D information visualisation in the real environment with immersive analytics. In this publication, we explore this potential by transforming 2D graph data from a planar, large-scale display in the real environment into a spherical layout in augmented reality 3D space, letting it appear as if the graph is moving out of the display. We focus on design aspects of this transformation that potentially help users to form a joint mental model of both visualisations and to continue their tasks seamlessly in augmented reality. For this purpose, we implemented a framework of transformation parameters that can be categorised as follows: transformation methods, node transformation order (groupings) and different ways of visual interconnection. Variants in each of these areas were investigated in three quantitative user studies in which users had to solve a simple cluster search task. We confirmed that a visual transformation from 2D to 3D helps users to continue their tasks in augmented reality with less interruptions, and that node transformation order should be adjusted to data and task context. We further identified that users can perform tasks more efficiently when a user-controlled transformation is used, while a constant transformation with fixed duration can contribute to lower error rates.

show abstract

“…This position was popular amongst works exploring spatial immersion by allowing users to explore multiple visualizations distributed throughout the virtual space [113]. Another scenario I observed unfixed positioning was for IA systems and visualizations where changing the visualizations' position was the primary method for changing the users' perspective [97].…”

Section: Positionmentioning

confidence: 99%

The mediums, the masses, the methods: towards meeting the demands of immersive analytics

Saffo

View full text Add to dashboard Cite

Immersive analytics (IA) is a developing sub-domain within the larger fields of information visualization, humancomputer interaction, and extended reality (XR). The focus of IA is to leverage emerging display technologies, such as virtual and augmented reality (VR, AR), to enhance data analysis and information visualization -using their unique affordances for 3D visual encoding, spatial interaction and presentation, and gesture controls. These affordances grant the potential for IA to enhance visualization workflows and tasks beyond what is possible with a 2D display. IA is a young research area, and XR devices, standards, practices, and software are relatively new and rapidly evolving. As a result, IA presents many research opportunities, and IA researchers studying these questions face several challenges. Existing literature has covered several challenges for traditional data visualization on 2D displays. However, data visualizations and their design, methods, and techniques are inherently linked to the mediums used to present them. As a result, traditional methods do not fully translate or encompass the needs of IA and the new medium of XR devices-motivating the need for new domain-specific research.IA demands novel design knowledge for research methods, collaboration modalities, and visualization design. This demand ultimately stems from the XR medium IA seeks to leverage and its relationship with what it can display, how visualizations are designed, and how users utilize it. Meeting the demand for this design knowledge is constrained by the vast and nascent nature of the IA design space, the difficulty of conducting IA design studies, the challenges of remote and collaborative XR user studies, and the complications of disparate display technologies. The work presented in this dissertation seeks to meet these demands and overcome these constraints. I do this through research contributing review analysis of the IA design space, design study methodology, remote user study methodology, and cross-virtually analytics collaboration. These works contribute new domain-specific knowledge, replicated results of real-life studies in VR, qualitative analysis of cross-platform collaborative IA, and a systematic review and analysis of the state-of-the-art in IA research. Through these contributions, this dissertation works towards meeting the demands of IA, allowing future research to be conducted more effectively and efficiently.iii AcknowledgementsIt is difficult to know where to begin acknowledging and thanking the countless people who made this dissertation possible. It's harder still knowing that the following words alone can never convey my gratitude to all. This dissertation is dedicated to all of you. It is a testament to the fact that it takes a village to raise a researcher and that we really stand on the shoulders of giants. I consider myself deeply lucky and privileged to have even had the opportunity to pursue a Ph.D. in computer science in the first place. I am deeply grateful for my early academic advisors, ...

show abstract

Visual Comparison of Networks in VR

Cited by 14 publications

References 66 publications

An Experimental Evaluation of Viewpoint‐Based 3D Graph Drawing

An Experimental Evaluation of Viewpoint‐Based 3D Graph Drawing

Transforming graph data visualisations from 2D displays into augmented reality 3D space: A quantitative study

The mediums, the masses, the methods: towards meeting the demands of immersive analytics

Contact Info

Product

Resources

About