44-Issue 3

Permanent URI for this collection

EuroVis 2025 - 27th EG Conference on Visualization
Luxembourg City, Luxembourg | June 2 - 6, 2025
Best Papers
NODKANT: Exploring Constructive Network Physicalization
Daniel Pahr, Sara Di Bartolomeo, Henry Ehlers, Velitchko Andreev Filipov, Christina Stoiber, Wolfgang Aigner, Hsiang-Yun Wu, and Renata Georgia Raidou
Honourable Mention
Gridded Visualization of Statistical Trees for High-Dimensional Multipartite Data in Systems Genetics
Jane L. Adams, Robyn L. Ball, Jason A. Bubier, Elissa J. Chesler, Melanie Tory, and Michelle A. Borkin
Optimizing Staircase Motifs in Biofabric Network Layouts
Sara Di Bartolomeo, Markus Wallinger, and Martin Nöllenburg
Flow Vis
HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
Hamid Gadirov, Qi Wu, David Bauer, Kwan-Liu Ma, Jos B.T.M. Roerdink, and Steffen Frey
In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection
Zhe Wang, Kenneth Moreland, Matthew Larsen, James Kress, Hank Childs, Guan Li, Guihua Shan, and David Pugmire
Enhancing Material Boundary Visualizations in 2D Unsteady Flow through Local Reference Frame Transformations
Xingdi Zhang, Peter Rautek, Thomas Theußl, and Markus Hadwiger
Explainable and Generative AI
IntelliCircos: A Data-driven and AI-powered Authoring Tool for Circos Plots
Mingyang Gu, Jiamin Zhu, Qipeng Wang, Fengjie Wang, Xiaolin Wen, Yong Wang, and Min Zhu
Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models
Johannes Eschner, Roberto Labadie-Tamayo, Matthias Zeppelzauer, and Manuela Waldner
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
Juntong Chen, Jiang Wu, Jiajing Guo, Vikram Mohanty, Xueming Li, Jorge Piazentin Ono, Wenbin He, Liu Ren, and Dongyu Liu
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Xinyuan Yan, Xiwei Xuan, Jorge Piazentin Ono, Jiajing Guo, Vikram Mohanty, Shekar Arvind Kumar, Liang Gou, Bei Wang, and Liu Ren
Sports, Gaming, and Behavioral Patterns
Beyond Entertainment: An Investigation of Externalization Design in Video Games
Franziska Becker, Rene Pascal Warnking, Hendrik Brückler, and Tanja Blascheck
Embedded and Situated Visualisation in Mixed Reality to Support Interval Running
Ang Li, Charles Perin, Jarrod Knibbe, Gianluca Demartini, Stephen Viller, and Maxime Cordeil
Player-Centric Shot Maps in Table Tennis
Aymeric Erades and Romain Vuillemot
Networks and Structures
Tasks and Visual Abstractions for 3D Chromatin Representation
Adam Rychlý, Jan Byška, Barbora Kozlikova, and Katarína Furmanová
Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations
Jacob Miller, Dhruv Bhatia, Helen Purchase, and Stephen Kobourov
Viewpoint Optimization for 3D Graph Drawings
Simon van Wageningen, Tamara Mchedlidze, and Alexandru Telea
Storytelling, Integration of Visualization and Text
DataWeaver: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text
Yu Fu, Dennis Bromley, and Vidya Setlur
Either Or: Interactive Articles or Videos for Climate Science Communication
Jeran Poehls, Monique Meuschke, Nuno Carvalhais, and Kai Lawonn
VIZTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface
Liangwei Wang, Zhan Wang, Shishi Xiao, Le Liu, Fugee Tsung, and Wei Zeng
DashGuide: Authoring Interactive Dashboard Tours for Guiding Dashboard Users
Naimul Hoque and Nicole Sultanum
AI-Enhanced Visualization
SUPQA: LLM-based Geo-Visualization for Subjective Urban Performance Question-Answering
Haiwen Huang, Juntong Chen, Changbo Wang, and Chenhui Li
Benchmarking Visual Language Models on Standardized Visualization Literacy Tests
Saugat Pandey and Alvitta Ottley
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
Rita Sevastjanova, Robin Gerling, Thilo Spinner, and Mennatallah El-Assady
An Interactive Visual Enhancement for Prompted Programmatic Weak Supervision in Text Classification
Yiming Lin, Shuqi Wei, Huijie Zhang, Dezhan Qu, and Jinghan Bai
Volume and Color
The Geometry of Color in the Light of a Non-Riemannian Space
Roxana Bujack, Emily N. Stark, Terece L. Turton, Jonah Maxwell Miller, and David H. Rogers
Random Access Segmentation Volume Compression for Interactive Volume Rendering
Max Piochowiak, Florian Kurpicz, and Carsten Dachsbacher
Fast and Invertible Simplicial Approximation of Magnetic-Following Interpolation for Visualizing Fusion Plasma Simulation Data
Congrong Ren, Robert Hager, Randy Michael Churchill, Albert Mollén, Seung-Hoe Ku, Choong-Seock Chang, and Hanqi Guo
Dimensionality Reduction and High-Dimensional Data
Necessary but not Sufficient: Limitations of Projection Quality Metrics
Alister Machado, Michael Behrisch, and Alexandru Telea
PrismBreak: Exploration of Multi-Dimensional Mixture Models
Brian Zahoransky, Tobias Günther, and Kai Lawonn
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
Fernando V. Paulovich, Alessio Arleo, and Stef van den Elzen
Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples
Ruben Bauer, Marina Evers, Quynh Quang Ngo, Guido Reina, Steffen Frey, and Michael Sedlmair
Uncertainty, Sensitivity, Scalability
Mapping Mental Models of Uncertainty to Parallel Coordinates by Probabilistic Brushing
Gabriel Borrelli, Till Ittermann, and Lars Linsen
Sca2Gri: Scalable Gridified Scatterplots
Steffen Frey
Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling
Tark Patel, Tushar M. Athawale, Timbwaoga A. J. Ouermi, and Chris R. Johnson
Spatial and Multi-Scale Data Visualization
Multipla: Multiscale Pangenomic Locus Analysis
Astrid van den Brandt, Emilia Ståhlbom, Fredericus Johannes Maria van Workum, Huub van de Wetering, Claes Lundström, Sandra Smit, and Anna Vilanova
Lactea: Web-Based Spectrum-Preserving Multi-Resolution Visualization of the GAIA Star Catalog
Reem Alghamdi, Markus Hadwiger, Guido Reina, and Alberto Jaspe-Villanueva
SurpriseExplora: Tuning and Contextualizing Model-derived Maps with Interactive Visualizations
Akim Ndlovu, Hilson Shrestha, Evan Peck, and Lane Harrison
Visually Assessing 1-D Orderings of Contiguous Spatial Polygons
Julius Rauscher, Frederik L. Dennig, Udo Schlegel, Daniel A. Keim, and Johannes Fuchs
Inclusive Visualization
FairSpace: An Interactive Visualization System for Constructing Fair Consensus from Many Rankings
Hilson Shrestha, Kathleen Cachel, Mallak Alkhathlan, Elke Rundensteiner, and Lane Harrison
MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks
Kai Nylund, Jennifer Mankoff, and Venkatesh Potluri
Instructional Comics for Self-Paced Learning of Data Visualization Tools and Concepts
Magdalena Boucher, Mashael AlKadi, Benjamin Bach, and Wolfgang Aigner
Accessible Text Descriptions for UpSet Plots
Andrew McNutt, Maggie K. McCracken, Ishrat Jahan Eliza, Daniel Hajas, Jake Wagoner, Nate Lanza, Jack Wilburn, Sarah Creem-Regehr, and Alexander Lex
Evaluation and Guidance
Modeling and Measuring the Chart Communication Recall Process
Anjana Arunkumar, Lace Padilla, and Chris Bryan
Coupling Guidance and Progressiveness in Visual Analytics
Ignacio Pérez-Messina, Marco Angelini, Davide Ceneda, Christian Tominski, and Silvia Miksch
Towards a Better Evaluation of 3D CVML Algorithms: Immersive Debugging of a Localization Model
Tica Lin, Jun Yuan, Kevin Miao, Tigran Katolikyan, Isaac Walker, and Marco Cavallo
A Process-Oriented Approach to Analyze Analysts' Use of Visualizations: Revealing Insights into the What, When, and How
Lisa Zimmerman, Francesca Zerbato, Katerina Vrotsou, and Barbara Weber

BibTeX (44-Issue 3)
                
@article{
10.1111:cgf.70143,
journal = {Computer Graphics Forum}, title = {{
EuroVis 2025 CGF 44-3: Frontmatter}},
author = {
Aigner, Wolfgang
and
Andrienko, Natalia
and
Wang, Bei
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70143}
}
                
@article{
10.1111:cgf.70098,
journal = {Computer Graphics Forum}, title = {{
DataWeaver: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text}},
author = {
Fu, Yu
and
Bromley, Dennis
and
Setlur, Vidya
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70098}
}
                
@article{
10.1111:cgf.70099,
journal = {Computer Graphics Forum}, title = {{
Modeling and Measuring the Chart Communication Recall Process}},
author = {
Arunkumar, Anjana
and
Padilla, Lace
and
Bryan, Chris
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70099}
}
                
@article{
10.1111:cgf.70100,
journal = {Computer Graphics Forum}, title = {{
Visually Assessing 1-D Orderings of Contiguous Spatial Polygons}},
author = {
Rauscher, Julius
and
Dennig, Frederik L.
and
Schlegel, Udo
and
Keim, Daniel A.
and
Fuchs, Johannes
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70100}
}
                
@article{
10.1111:cgf.70101,
journal = {Computer Graphics Forum}, title = {{
Necessary but not Sufficient: Limitations of Projection Quality Metrics}},
author = {
Machado, Alister
and
Behrisch, Michael
and
Telea, Alexandru
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70101}
}
                
@article{
10.1111:cgf.70102,
journal = {Computer Graphics Forum}, title = {{
Accessible Text Descriptions for UpSet Plots}},
author = {
McNutt, Andrew
and
McCracken, Maggie K.
and
Eliza, Ishrat Jahan
and
Hajas, Daniel
and
Wagoner, Jake
and
Lanza, Nate
and
Wilburn, Jack
and
Creem-Regehr, Sarah
and
Lex, Alexander
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70102}
}
                
@article{
10.1111:cgf.70103,
journal = {Computer Graphics Forum}, title = {{
Mapping Mental Models of Uncertainty to Parallel Coordinates by Probabilistic Brushing}},
author = {
Borrelli, Gabriel
and
Ittermann, Till
and
Linsen, Lars
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70103}
}
                
@article{
10.1111:cgf.70104,
journal = {Computer Graphics Forum}, title = {{
A Process-Oriented Approach to Analyze Analysts' Use of Visualizations: Revealing Insights into the What, When, and How}},
author = {
Zimmerman, Lisa
and
Zerbato, Francesca
and
Vrotsou, Katerina
and
Weber, Barbara
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70104}
}
                
@article{
10.1111:cgf.70105,
journal = {Computer Graphics Forum}, title = {{
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities}},
author = {
Paulovich, Fernando V.
and
Arleo, Alessio
and
Elzen, Stef van den
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70105}
}
                
@article{
10.1111:cgf.70106,
journal = {Computer Graphics Forum}, title = {{
SUPQA: LLM-based Geo-Visualization for Subjective Urban Performance Question-Answering}},
author = {
Huang, Haiwen
and
Chen, Juntong
and
Wang, Changbo
and
Li, Chenhui
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70106}
}
                
@article{
10.1111:cgf.70107,
journal = {Computer Graphics Forum}, title = {{
DashGuide: Authoring Interactive Dashboard Tours for Guiding Dashboard Users}},
author = {
Hoque, Naimul
and
Sultanum, Nicole
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70107}
}
                
@article{
10.1111:cgf.70108,
journal = {Computer Graphics Forum}, title = {{
In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection}},
author = {
Wang, Zhe
and
Moreland, Kenneth
and
Larsen, Matthew
and
Kress, James
and
Childs, Hank
and
Li, Guan
and
Shan, Guihua
and
Pugmire, David
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70108}
}
                
@article{
10.1111:cgf.70109,
journal = {Computer Graphics Forum}, title = {{
Player-Centric Shot Maps in Table Tennis}},
author = {
Erades, Aymeric
and
Vuillemot, Romain
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70109}
}
                
@article{
10.1111:cgf.70110,
journal = {Computer Graphics Forum}, title = {{
VIZTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface}},
author = {
Wang, Liangwei
and
Wang, Zhan
and
Xiao, Shishi
and
Liu, Le
and
Tsung, Fugee
and
Zeng, Wei
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70110}
}
                
@article{
10.1111:cgf.70111,
journal = {Computer Graphics Forum}, title = {{
Towards a Better Evaluation of 3D CVML Algorithms: Immersive Debugging of a Localization Model}},
author = {
Lin, Tica
and
Yuan, Jun
and
Miao, Kevin
and
Katolikyan, Tigran
and
Walker, Isaac
and
Cavallo, Marco
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70111}
}
                
@article{
10.1111:cgf.70112,
journal = {Computer Graphics Forum}, title = {{
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions}},
author = {
Chen, Juntong
and
Wu, Jiang
and
Guo, Jiajing
and
Mohanty, Vikram
and
Li, Xueming
and
Ono, Jorge Piazentin
and
He, Wenbin
and
Ren, Liu
and
Liu, Dongyu
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70112}
}
                
@article{
10.1111:cgf.70113,
journal = {Computer Graphics Forum}, title = {{
Gridded Visualization of Statistical Trees for High-Dimensional Multipartite Data in Systems Genetics}},
author = {
Adams, Jane L.
and
Ball, Robyn L.
and
Bubier, Jason A.
and
Chesler, Elissa J.
and
Tory, Melanie
and
Borkin, Michelle A.
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70113}
}
                
@article{
10.1111:cgf.70114,
journal = {Computer Graphics Forum}, title = {{
SurpriseExplora: Tuning and Contextualizing Model-derived Maps with Interactive Visualizations}},
author = {
Ndlovu, Akim
and
Shrestha, Hilson
and
Peck, Evan
and
Harrison, Lane
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70114}
}
                
@article{
10.1111:cgf.70115,
journal = {Computer Graphics Forum}, title = {{
Coupling Guidance and Progressiveness in Visual Analytics}},
author = {
Pérez-Messina, Ignacio
and
Angelini, Marco
and
Ceneda, Davide
and
Tominski, Christian
and
Miksch, Silvia
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70115}
}
                
@article{
10.1111:cgf.70116,
journal = {Computer Graphics Forum}, title = {{
Random Access Segmentation Volume Compression for Interactive Volume Rendering}},
author = {
Piochowiak, Max
and
Kurpicz, Florian
and
Dachsbacher, Carsten
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70116}
}
                
@article{
10.1111:cgf.70117,
journal = {Computer Graphics Forum}, title = {{
Lactea: Web-Based Spectrum-Preserving Multi-Resolution Visualization of the GAIA Star Catalog}},
author = {
Alghamdi, Reem
and
Hadwiger, Markus
and
Reina, Guido
and
Jaspe-Villanueva, Alberto
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70117}
}
                
@article{
10.1111:cgf.70118,
journal = {Computer Graphics Forum}, title = {{
IntelliCircos: A Data-driven and AI-powered Authoring Tool for Circos Plots}},
author = {
Gu, Mingyang
and
Zhu, Jiamin
and
Wang, Qipeng
and
Wang, Fengjie
and
Wen, Xiaolin
and
Wang, Yong
and
Zhu, Min
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70118}
}
                
@article{
10.1111:cgf.70119,
journal = {Computer Graphics Forum}, title = {{
MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks}},
author = {
Nylund, Kai
and
Mankoff, Jennifer
and
Potluri, Venkatesh
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70119}
}
                
@article{
10.1111:cgf.70120,
journal = {Computer Graphics Forum}, title = {{
Fast and Invertible Simplicial Approximation of Magnetic-Following Interpolation for Visualizing Fusion Plasma Simulation Data}},
author = {
Ren, Congrong
and
Hager, Robert
and
Churchill, Randy Michael
and
Mollén, Albert
and
Ku, Seung-Hoe
and
Chang, Choong-Seock
and
Guo, Hanqi
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70120}
}
                
@article{
10.1111:cgf.70121,
journal = {Computer Graphics Forum}, title = {{
PrismBreak: Exploration of Multi-Dimensional Mixture Models}},
author = {
Zahoransky, Brian
and
Günther, Tobias
and
Lawonn, Kai
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70121}
}
                
@article{
10.1111:cgf.70122,
journal = {Computer Graphics Forum}, title = {{
Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples}},
author = {
Bauer, Ruben
and
Evers, Marina
and
Ngo, Quynh Quang
and
Reina, Guido
and
Frey, Steffen
and
Sedlmair, Michael
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70122}
}
                
@article{
10.1111:cgf.70123,
journal = {Computer Graphics Forum}, title = {{
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections}},
author = {
Sevastjanova, Rita
and
Gerling, Robin
and
Spinner, Thilo
and
El-Assady, Mennatallah
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70123}
}
                
@article{
10.1111:cgf.70124,
journal = {Computer Graphics Forum}, title = {{
Beyond Entertainment: An Investigation of Externalization Design in Video Games}},
author = {
Becker, Franziska
and
Warnking, Rene Pascal
and
Brückler, Hendrik
and
Blascheck, Tanja
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70124}
}
                
@article{
10.1111:cgf.70125,
journal = {Computer Graphics Forum}, title = {{
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis}},
author = {
Yan, Xinyuan
and
Xuan, Xiwei
and
Ono, Jorge Piazentin
and
Guo, Jiajing
and
Mohanty, Vikram
and
Kumar, Shekar Arvind
and
Gou, Liang
and
Wang, Bei
and
Ren, Liu
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70125}
}
                
@article{
10.1111:cgf.70126,
journal = {Computer Graphics Forum}, title = {{
Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations}},
author = {
Miller, Jacob
and
Bhatia, Dhruv
and
Purchase, Helen
and
Kobourov, Stephen
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70126}
}
                
@article{
10.1111:cgf.70127,
journal = {Computer Graphics Forum}, title = {{
Viewpoint Optimization for 3D Graph Drawings}},
author = {
Wageningen, Simon van
and
Mchedlidze, Tamara
and
Telea, Alexandru
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70127}
}
                
@article{
10.1111:cgf.70128,
journal = {Computer Graphics Forum}, title = {{
Enhancing Material Boundary Visualizations in 2D Unsteady Flow through Local Reference Frame Transformations}},
author = {
Zhang, Xingdi
and
Rautek, Peter
and
Theußl, Thomas
and
Hadwiger, Markus
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70128}
}
                
@article{
10.1111:cgf.70129,
journal = {Computer Graphics Forum}, title = {{
Either Or: Interactive Articles or Videos for Climate Science Communication}},
author = {
Poehls, Jeran
and
Meuschke, Monique
and
Carvalhais, Nuno
and
Lawonn, Kai
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70129}
}
                
@article{
10.1111:cgf.70130,
journal = {Computer Graphics Forum}, title = {{
Instructional Comics for Self-Paced Learning of Data Visualization Tools and Concepts}},
author = {
Boucher, Magdalena
and
AlKadi, Mashael
and
Bach, Benjamin
and
Aigner, Wolfgang
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70130}
}
                
@article{
10.1111:cgf.70131,
journal = {Computer Graphics Forum}, title = {{
An Interactive Visual Enhancement for Prompted Programmatic Weak Supervision in Text Classification}},
author = {
Lin, Yiming
and
Wei, Shuqi
and
Zhang, Huijie
and
Qu, Dezhan
and
Bai, Jinghan
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70131}
}
                
@article{
10.1111:cgf.70132,
journal = {Computer Graphics Forum}, title = {{
FairSpace: An Interactive Visualization System for Constructing Fair Consensus from Many Rankings}},
author = {
Shrestha, Hilson
and
Cachel, Kathleen
and
ALKHATHLAN, MALLAK
and
Rundensteiner, Elke
and
Harrison, Lane
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70132}
}
                
@article{
10.1111:cgf.70133,
journal = {Computer Graphics Forum}, title = {{
Embedded and Situated Visualisation in Mixed Reality to Support Interval Running}},
author = {
Li, Ang
and
Perin, Charles
and
Knibbe, Jarrod
and
Demartini, Gianluca
and
Viller, Stephen
and
Cordeil, Maxime
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70133}
}
                
@article{
10.1111:cgf.70134,
journal = {Computer Graphics Forum}, title = {{
HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization}},
author = {
Gadirov, Hamid
and
Wu, Qi
and
Bauer, David
and
Ma, Kwan-Liu
and
Roerdink, Jos B.T.M.
and
Frey, Steffen
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70134}
}
                
@article{
10.1111:cgf.70135,
journal = {Computer Graphics Forum}, title = {{
Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models}},
author = {
Eschner, Johannes
and
Labadie-Tamayo, Roberto
and
Zeppelzauer, Matthias
and
Waldner, Manuela
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70135}
}
                
@article{
10.1111:cgf.70136,
journal = {Computer Graphics Forum}, title = {{
The Geometry of Color in the Light of a Non-Riemannian Space}},
author = {
Bujack, Roxana
and
Stark, Emily N.
and
Turton, Terece L.
and
Miller, Jonah Maxwell
and
Rogers, David H.
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70136}
}
                
@article{
10.1111:cgf.70137,
journal = {Computer Graphics Forum}, title = {{
Benchmarking Visual Language Models on Standardized Visualization Literacy Tests}},
author = {
Pandey, Saugat
and
Ottley, Alvitta
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70137}
}
                
@article{
10.1111:cgf.70138,
journal = {Computer Graphics Forum}, title = {{
Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling}},
author = {
Patel, Tark
and
Athawale, Tushar M.
and
Ouermi, Timbwaoga A. J.
and
Johnson, Chris R.
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70138}
}
                
@article{
10.1111:cgf.70139,
journal = {Computer Graphics Forum}, title = {{
Optimizing Staircase Motifs in Biofabric Network Layouts}},
author = {
Bartolomeo, Sara Di
and
Wallinger, Markus
and
Nöllenburg, Martin
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70139}
}
                
@article{
10.1111:cgf.70140,
journal = {Computer Graphics Forum}, title = {{
NODKANT: Exploring Constructive Network Physicalization}},
author = {
Pahr, Daniel
and
Bartolomeo, Sara Di
and
Ehlers, Henry
and
Filipov, Velitchko Andreev
and
Stoiber, Christina
and
Aigner, Wolfgang
and
Wu, Hsiang-Yun
and
Raidou, Renata Georgia
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70140}
}
                
@article{
10.1111:cgf.70141,
journal = {Computer Graphics Forum}, title = {{
Sca2Gri: Scalable Gridified Scatterplots}},
author = {
Frey, Steffen
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70141}
}
                
@article{
10.1111:cgf.70142,
journal = {Computer Graphics Forum}, title = {{
Tasks and Visual Abstractions for 3D Chromatin Representation}},
author = {
Rychlý, Adam
and
Byška, Jan
and
Kozlikova, Barbora
and
Furmanová, Katarína
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70142}
}
                
@article{
10.1111:cgf.70147,
journal = {Computer Graphics Forum}, title = {{
Multipla: Multiscale Pangenomic Locus Analysis}},
author = {
Brandt, Astrid van den
and
Ståhlbom, Emilia
and
Workum, Fredericus Johannes Maria van
and
Wetering, Huub van de
and
Lundström, Claes
and
Smit, Sandra
and
Vilanova, Anna
}, year = {
2025},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.70147}
}

Browse

Recent Submissions

Now showing 1 - 47 of 47
  • Item
    EuroVis 2025 CGF 44-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
  • Item
    DataWeaver: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Fu, Yu; Bromley, Dennis; Setlur, Vidya; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, DATAWEAVER, that supports both visualization-to-text and text-to-visualization composition. DATAWEAVER enables users to create data narratives anchored to data facts derived from ''call-out'' interactions, i.e., user-initiated highlights of visualization elements that prompt relevant narrative content. In addition to this ''vis-to-text'' composition, DATAWEAVER also supports a ''text-initiated'' approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of DATAWEAVER and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.
  • Item
    Modeling and Measuring the Chart Communication Recall Process
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Arunkumar, Anjana; Padilla, Lace; Bryan, Chris; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Understanding memory in the context of data visualizations is paramount for effective design. While immediate clarity in a visualization is crucial, retention of its information determines its long-term impact. While extensive research has underscored the elements enhancing visualization memorability, a limited body of work has delved into modeling the recall process. This study investigates the temporal dynamics of visualization recall, focusing on factors influencing recollection, shifts in recall veracity, and the role of participant demographics. Using data from an empirical study (n = 104), we propose a novel approach combining temporal clustering and handcrafted features to model recall over time. A long short-term memory (LSTM) model with attention mechanisms predicts recall patterns, revealing alignment with informativeness scores and participant characteristics. Our findings show that perceived informativeness dictates recall focus, with more informative visualizations eliciting narrative-driven insights and less informative ones prompting aesthetic-driven responses. Recall accuracy diminishes over time, particularly for unfamiliar visualizations, with age and education significantly shaping recall emphases. These insights advance our understanding of visualization recall, offering practical guidance for designing visualizations that enhance retention and comprehension. All data and materials are available at: https://osf.io/ghe2j/.
  • Item
    Visually Assessing 1-D Orderings of Contiguous Spatial Polygons
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Rauscher, Julius; Dennig, Frederik L.; Schlegel, Udo; Keim, Daniel A.; Fuchs, Johannes; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    One-dimensional orderings of spatial entities have been researched in many contexts, e.g. spatial indexing structures or visualizations for spatiotemporal trend analysis. While plenty of studies have been conducted to evaluate orderings of point-based data, polygonal shapes, despite their different topological properties, have received less attention. Existing measures to quantify errors in projections or orderings suffer from generic neighborhood definitions and over-simplification of distances when applied to polygonal data. In this work, we address these shortcomings by introducing measures that adapt to a varying neighborhood size depending on the number of contiguous neighbors and thus, address the limitations of existing measures for polygonal shapes. To guide experts in determining a suitable ordering, we propose a user-steerable visual analytics prototype capable of locally and globally inspecting ordering errors, investigating the impact of geographic obstacles, and comparing ordering strategies using our measures.We demonstrate the effectiveness of our approach through a use case and conducted an expert study with 8 data scientists as a qualitative evaluation of our approach. Our results show that users are capable of identifying ordering errors, comparing ordering strategies on a global and local scale, as well as assessing the impact of semantically relevant geographic obstacles.
  • Item
    Necessary but not Sufficient: Limitations of Projection Quality Metrics
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Machado, Alister; Behrisch, Michael; Telea, Alexandru; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    High-dimensional data analysis often uses dimensionality reduction (DR, also called projection) to map data patterns to human-digestible visual patterns in a 2D scatterplot. Yet, DR methods may fail to show true data patterns and/or create visual patterns that do not represent any data patterns. Projection Quality Metrics (PQMs) are used as objective measures to gauge the above process: the higher a projection's scores in PQMs, the more it is deemed faithful to the data it represents. We show that, while PQMs can be used as exclusion criteria - low values usually mean poor projections - the converse does not always hold. For this, we develop a technique to automatically generate projections that score similar or even higher PQM values than projections created by well-known techniques, but show different, often confusing, visual patterns. Our results show that accepted PQMs cannot be used as an exclusive way to tell whether a projection yields accurate and interpretable visual patterns - in this sense, PQMs play a role akin to that of summary statistics in exploratory data analysis. We also show that not all studied metrics can be fooled equally well, suggesting a ranking of metrics in their ability to reliably capture quality.
  • Item
    Accessible Text Descriptions for UpSet Plots
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) McNutt, Andrew; McCracken, Maggie K.; Eliza, Ishrat Jahan; Hajas, Daniel; Wagoner, Jake; Lanza, Nate; Wilburn, Jack; Creem-Regehr, Sarah; Lex, Alexander; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Data visualizations are typically not accessible to blind and low-vision (BLV) users. Automatically generating text descriptions offers an enticing mechanism for democratizing access to the information held in complex scientific charts, yet appropriate procedures for generating those texts remain elusive. Pursuing this issue, we study a single complex chart form: UpSet plots. UpSet Plots are a common way to analyze set data, an area largely unexplored by prior accessibility literature. By analyzing the patterns present in real-world examples, we develop a system for automatically captioning any UpSet plot. We evaluated the utility of our captions via semi-structured interviews with (N=11) BLV users and found that BLV users find them informative. In extensions, we find that sighted users can use our texts similarly to UpSet plots and that they are better than naive LLM usage.
  • Item
    Mapping Mental Models of Uncertainty to Parallel Coordinates by Probabilistic Brushing
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Borrelli, Gabriel; Ittermann, Till; Linsen, Lars; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Through training and gathered experience, domain experts attain a mental model of the uncertainties inherent in the visual analytics processes for their respective domain. For an accurate data analysis and trustworthiness of the analysis results, it is essential to include this knowledge and consider this model of uncertainty during the analytical process. For multi-dimensional data analysis, Parallel Coordinates are a widely used approach due to their linear scalability with the number of dimensions and bijective (i.e., loss-less) data transformation. However, selections in Parallel Coordinates are typically achieved by a binary brushing operation on the axes, which does not allow the users to map their mental model of uncertainties to their selection. We, therefore, propose Probabilistic Parallel Coordinates as a natural extension of the classical Parallel Coordinates approach that integrates probabilistic brushing on the axes. It supports the interactive modeling of a probability distribution for each parallel coordinate. The selections on multiple axes are combined accordingly. An efficient rendering on a compute shader facilitates interactive frame rates. We evaluated our open-source tool with practitioners and compared it to classical Parallel Coordinates on multiple regression and uncertain selection tasks in user studies.
  • Item
    A Process-Oriented Approach to Analyze Analysts' Use of Visualizations: Revealing Insights into the What, When, and How
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Zimmerman, Lisa; Zerbato, Francesca; Vrotsou, Katerina; Weber, Barbara; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Despite Visual Analytics (VA) tools being essential for supporting data analysis, evaluating their use in real-world analytical processes remains challenging. Traditional evaluation methods often overlook the nuanced and evolving nature of analysis processes and are not always suitable for investigating scenarios in which analysts combine multiple tools and visualization types. In this paper, we propose a flexible analysis approach for studying analysts' use of visualizations within and across VA tools. Our qualitative method allows researchers to extract user behavior and cognitive steps from screen recordings and think-aloud data and generate event sequences that capture analytic processes. This enables the analysis of usage patterns from multiple perspectives and levels of granularity and allows for the evaluation of effectiveness measures, such as efficiency and accuracy. We demonstrate our approach in the domain of process mining, where our findings provide insights into the use of existing visualizations, and we reflect on lessons learned from this application.
  • Item
    When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Paulovich, Fernando V.; Arleo, Alessio; Elzen, Stef van den; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math-grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well-defined stages, discuss how to match some of the DR state-of-the-art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.
  • Item
    SUPQA: LLM-based Geo-Visualization for Subjective Urban Performance Question-Answering
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Huang, Haiwen; Chen, Juntong; Wang, Changbo; Li, Chenhui; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    As urbanization accelerates, urban performance has become a growing concern, impacting every aspect of residents' lives. However, urban performance exploration is a tedious and highly subjective process for users. Users need to manually collect and integrate various information, or spend a large amount of time and effort due to the steep learning curves of existing specialized tools. To address these challenges, we introduce SUPQA, a novel approach for urban performance exploration using natural language as input and interactive geographic visualizations as output. Our approach leverages Large Language Models (LLMs) to effectively interpret user intents and quantify various urban performance measures. We integrate progressive navigation and multi-geographic scale analysis in our visualization system, explaining the reasoning process and streamlining users' decision-making workflow. Two usage scenarios and evaluations demonstrate the effectiveness of SUPQA in helping residents and planners acquire desired information more efficiently and enhancing the quality of decision-making.
  • Item
    DashGuide: Authoring Interactive Dashboard Tours for Guiding Dashboard Users
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Hoque, Naimul; Sultanum, Nicole; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Dashboard guidance helps dashboard users better navigate interactive features, understand the underlying data, and assess insights they can potentially extract from dashboards. However, authoring dashboard guidance is a time consuming task, and embedding guidance into dashboards for effective delivery is difficult to realize. In this work, we contribute DASHGUIDE, a framework and system to support the creation of interactive dashboard guidance with minimal authoring input. Given a dashboard and a communication goal, DASHGUIDE captures a sequence of author-performed interactions to generate guidance materials delivered as playable step-by-step overlays, a.k.a., dashboard tours. Authors can further edit and refine individual tour steps while receiving generative assistance. We also contribute findings from a formative assessment with 9 dashboard creators, which helped inform the design of DASHGUIDE; and findings from an evaluation of DASHGUIDE with 12 dashboard creators, suggesting it provides an improved authoring experience that balances efficiency, expressiveness, and creative freedom.
  • Item
    In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Zhe; Moreland, Kenneth; Larsen, Matthew; Kress, James; Childs, Hank; Li, Guan; Shan, Guihua; Pugmire, David; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.
  • Item
    Player-Centric Shot Maps in Table Tennis
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Erades, Aymeric; Vuillemot, Romain; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Shot maps are popular in many sports as they typically plot events and player positions in the way they are collected, using a pitch or a table as an absolute coordinate system. We introduce a variation of a table tennis shot map that shifts the point of view from the table to the player. This results in a new reference system to plot incoming balls relative to the player's position rather than on the table. This approach aligns with how table tennis tactical analysis is conducted, focusing on identifying empty spaces and weak spots around the players. We describe the motivation behind this work, built through close collaboration with two table tennis experts, and demonstrate how this approach aligns with the way they analyze games to reveal key tactical aspects. We also present the design rationale and the computer vision pipeline used to accurately collect data from broadcast videos. Our findings show that the technique enables capturing insights that were not visible with the absolute coordinate system, particularly in understanding regions that are reachable and those close to the pivot area of the player.
  • Item
    VIZTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Liangwei; Wang, Zhan; Xiao, Shishi; Liu, Le; Tsung, Fugee; Zeng, Wei; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM-based conversational interfaces show promise in promoting visualization comprehension. However, they fail to provide contextual explanations at fine-grained granularity, and chart readers are still required to mentally bridge visual information and textual explanations during conversations. Our formative study highlights the expectations for both lexical and visual feedback, as well as the importance of explicitly linking these two modalities throughout the conversation. The findings motivate the design of VIZTA, a visualization teaching assistant that leverages the fusion of visual and lexical feedback to help readers better comprehend visualization. VIZTA features a semantic-aware conversational agent capable of explaining contextual information within visualizations and employs a visual-lexical fusion design to facilitate chart-centered conversation. A between-subject study with 24 participants demonstrates the effectiveness of VIZTA in supporting the understanding and reasoning tasks of distributional visualization across multiple scenarios.
  • Item
    Towards a Better Evaluation of 3D CVML Algorithms: Immersive Debugging of a Localization Model
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Lin, Tica; Yuan, Jun; Miao, Kevin; Katolikyan, Tigran; Walker, Isaac; Cavallo, Marco; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    As advancements in robotics, autonomous driving, and spatial computing continue to unfold, a growing number of Computer Vision and Machine Learning (CVML) algorithms are incorporating three-dimensional data into their frameworks. Debugging these 3D CVML models often requires going beyond traditional performance evaluation methods, necessitating a deeper understanding of an algorithm's behavior within its spatio-temporal context. However, the lack of appropriate visualization tools presents a significant obstacle to effectively exploring 3D data and spatial features in relation to key performance indicators (KPIs). To address this challenge, we explore the application of Immersive Analytics (IA) methodologies to enhance the debugging process of 3D CVML models. Through in-depth interviews with eight CVML engineers, we identify common tasks and challenges faced during the development of spatial algorithms, and establish a set of design principles for creating tools tailored to spatial model evaluation. Building on these insights, we propose a novel immersive analytics system for debugging an indoor localization algorithm. The system is built using web technologies and integrates WebXR to enable fluid transitions across the reality-virtuality continuum. We conduct a qualitative study with six CVML engineers using our system on Apple Vision Pro, observing their analytical workflow as they debug an indoor localization sequence. We discuss the advantages of employing immersive analytics in the model evaluation workflow, emphasizing the role of seamlessly integrating 2D and 3D visualizations across varying levels of immersion to facilitate more effective model assessment. Finally, we reflect on the implementation trade-offs and discuss the generalizability of our findings for future efforts in immersive 3D CVML model debugging.
  • Item
    InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Chen, Juntong; Wu, Jiang; Guo, Jiajing; Mohanty, Vikram; Li, Xueming; Ono, Jorge Piazentin; He, Wenbin; Ren, Liu; Liu, Dongyu; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data-driven insights, yet significant challenges persist in accurately interpreting users analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error-prone, and time-intensive. To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions. Building on these insights, we introduce a highly extensible workflow that integrates multiple LLM agents for intent inference and visualization generation.We develop InterChat, a generative visual analytics system that combines direct manipulation of visual elements with natural language inputs. This integration enables precise intent communication and supports progressive, visually driven exploratory data analyses. By employing effective prompt engineering, and contextual interaction linking, alongside intuitive visualization and interaction designs, InterChat bridges the gap between user interactions and LLM-driven visualizations, enhancing both interpretability and usability. Extensive evaluations, including two usage scenarios, a user study, and expert feedback, demonstrate the effectiveness of InterChat. Results show significant improvements in the accuracy and efficiency of handling complex visual analytics tasks, highlighting the potential of multimodal interactions to redefine user engagement and analytical depth in generative visual analytics.
  • Item
    Gridded Visualization of Statistical Trees for High-Dimensional Multipartite Data in Systems Genetics
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Adams, Jane L.; Ball, Robyn L.; Bubier, Jason A.; Chesler, Elissa J.; Tory, Melanie; Borkin, Michelle A.; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    In systems genetics and other multi-omics research, exploring high-dimensional relationships among molecular and physiological variables across individuals poses significant challenges. We present the Gridded Trees interface, a novel interactive visualization tool designed to facilitate the exploration of conditional inference trees, which are hierarchical models of relationships in these complex datasets. Traditional static tools struggle to reveal patterns in tree-structured data, but the Gridded Trees interface provides interactive, coordinated views, allowing users to navigate between overview and detail, filter data dynamically, and compare molecular-physiological relationships across subgroups. By combining filtering techniques, strip plots, Sankey diagrams, and small multiples, the Gridded Trees interface enhances exploratory data analysis and supports hypothesis generation. In our systems genetics research use case, this tool has revealed significant associations among microbial populations and addiction-related behavioral traits in genetically diverse mice. The Gridded Trees interface suggests broad potential for visualizing hierarchical and multipartite data across domains. A preprint of this paper as well as Supplemental Materials are available on OSF at https://osf.io/9emn5/.
  • Item
    SurpriseExplora: Tuning and Contextualizing Model-derived Maps with Interactive Visualizations
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Ndlovu, Akim; Shrestha, Hilson; Peck, Evan; Harrison, Lane; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    People craft choropleth maps to monitor, analyze, and understand spatially distributed data. Recent visualization work has addressed several known biases in choropleth maps by developing new model- and metrics- based approaches (e.g. Bayesian surprise). However, effective use of these techniques requires extensive parameter setting and tuning, making them difficult or impossible for users without substantial technical skills. In this paper we describe SurpriseExplora, which addresses this gap through direct manipulation techniques for re-targeting a Bayesian surprise model's scope and parameters. We present three use cases to illustrate the capabilities of SurpriseExplora, showing for example how models calculated at a national level can obscure key findings that can be revealed through interaction sequences common to map visualizations (e.g. zooming), and how augmenting funnel-plot visualizations with interactions that adjust underlying models can account for outliers or skews in spatial datasets. We evaluate SurpriseExplora through an expert review with visualization researchers and practitioners. We conclude by discussing how SurpriseExplora uncovers new opportunities for sense-making within the broader ecosystem of map visualizations, as well as potential empirical studies with non-expert populations. Code and demo video available at https://osf.io/7m89w/
  • Item
    Coupling Guidance and Progressiveness in Visual Analytics
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Pérez-Messina, Ignacio; Angelini, Marco; Ceneda, Davide; Tominski, Christian; Miksch, Silvia; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Data size and complexity in Visual Analytics (VA) pose significant challenges for VA systems and VA users. Two recent developments address these challenges: progressive VA (PVA) and guidance for VA (GVA). Both share the goal of supporting the analysis flow. PVA primarily considers the system perspective and incrementally generates partial results during long computations to avoid an unresponsive VA system. GVA is primarily concerned with the user perspective and strives to mitigate knowledge gaps during VA activities to prevent the analysis from stalling. Although PVA and GVA share the same goal, it has not yet been studied how PVA and GVA can join forces to achieve it. Our paper investigates this in detail. We structure our research around two questions: How can guidance enhance PVA and how can progressiveness enhance GVA? This leads to two main themes: Guidance for Progressiveness (G4P) and Progressiveness for Guidance (P4G). By exploring both themes, we arrive at a conceptual model of how progressiveness and guidance can work together. We illustrate the practical value of our theoretical considerations in two case studies of G4P and P4G.
  • Item
    Random Access Segmentation Volume Compression for Interactive Volume Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Piochowiak, Max; Kurpicz, Florian; Dachsbacher, Carsten; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Segmentation volumes are voxel data sets often used in machine learning, connectomics, and natural sciences. Their large sizes make compression indispensable for storage and processing, including GPU video memory constrained real-time visualization. Fast Compressed Segmentation Volumes (CSGV) [PD24] provide strong brick-wise compression and random access at the brick level. Voxels within a brick, however, have to be decoded serially and thus rendering requires caching of visible full bricks, consuming extra memory. Without caching, accessing voxels can have a worst-case decoding overhead of up to a full brick (typically over 32.000 voxels). We present CSGV-R which provide true multi-resolution random access on a per-voxel level. We leverage Huffman-shaped Wavelet Trees for random accesses to variable bit-length encoding and their rank operation to query label palette offsets in bricks. Our real-time segmentation volume visualization removes decoding artifacts from CSGV and renders CSGV-R volumes without caching bricks at faster render times. CSGV-R has slightly lower compression rates than CSGV, but outperforms Neuroglancer, the state-of-the-art compression technique with true random access, with 2× to 4× smaller data sets at rates between 0.648% and 4.411% of the original volume sizes.
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    Lactea: Web-Based Spectrum-Preserving Multi-Resolution Visualization of the GAIA Star Catalog
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Alghamdi, Reem; Hadwiger, Markus; Reina, Guido; Jaspe-Villanueva, Alberto; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    The explosion of data in astronomy has resulted in an era of unprecedented opportunities for discovery. The GAIA mission's catalog, containing a large number of light sources (mostly stars) with several parameters such as sky position and proper motion, is playing a significant role in advancing astronomy research and has been crucial in various scientific breakthroughs over the past decade. In its current release, more than 200 million stars contain a calibrated continuous spectrum, which is essential for characterizing astronomical information such as effective temperature and surface gravity, and enabling complex tasks like interstellar extinction detection and narrow-band filtering. Even though numerous studies have been conducted to visualize and analyze the data in the SciVis and AstroVis communities, no work has attempted to leverage spectral information for visualization in real-time. Interactive exploration of such complex, massive data presents several challenges for visualization. This paper introduces a novel multi-resolution, spectrum-preserving data structure and a progressive, real-time visualization algorithm to handle the sheer volume of the data efficiently, enabling interactive visualization and exploration of the whole catalog's spectra. We show the efficiency of our method with our open-source, interactive, web-based tool for exploring the GAIA catalog, and discuss astronomically relevant use cases of our system.
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    IntelliCircos: A Data-driven and AI-powered Authoring Tool for Circos Plots
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Gu, Mingyang; Zhu, Jiamin; Wang, Qipeng; Wang, Fengjie; Wen, Xiaolin; Wang, Yong; Zhu, Min; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Genomics data is essential in biological and medical domains, and bioinformatics analysts often manually create circos plots to analyze the data and extract valuable insights. However, creating circos plots is complex, as it requires careful design for multiple track attributes and positional relationships between them. Typically, analysts often seek inspiration from existing circos plots, and they have to iteratively adjust and refine the plot to achieve a satisfactory final design, making the process both tedious and time-intensive. To address these challenges, we propose IntelliCircos, an AI-powered interactive authoring tool that streamlines the process from initial visual design to the final implementation of circos plots. Specifically, we build a new dataset containing 4396 circos plots with corresponding annotations and configurations, which are extracted and labeled from published papers. With the dataset, we further identify track combination patterns, and utilize Large Language Model (LLM) to provide domain-specific design recommendations and configuration references to navigate the design of circos plots. We conduct a user study with 8 bioinformatics analysts to evaluate IntelliCircos, and the results demonstrate its usability and effectiveness in authoring circos plots.
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    MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Nylund, Kai; Mankoff, Jennifer; Potluri, Venkatesh; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    We present MatplotAlt, an open-source Python package for easily adding alternative text to Matplotlib fgures. MatplotAlt equips Jupyter notebook authors to automatically generate and surface chart descriptions with a single line of code or command, and supports a range of options that allow users to customize the generation and display of captions based on their preferences and accessibility needs. Our evaluation indicates that MatplotAlt's heuristic and LLM-based methods to generate alt text can create accurate long-form descriptions of both simple univariate and complex Matplotlib fgures. We fnd that state-of-the-art LLMs still struggle with factual errors when describing charts, and improve the accuracy of our descriptions by prompting GPT4-turbo with heuristic-based alt text or data tables parsed from the Matplotlib fgure.
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    Fast and Invertible Simplicial Approximation of Magnetic-Following Interpolation for Visualizing Fusion Plasma Simulation Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Ren, Congrong; Hager, Robert; Churchill, Randy Michael; Mollén, Albert; Ku, Seung-Hoe; Chang, Choong-Seock; Guo, Hanqi; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    We introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.
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    PrismBreak: Exploration of Multi-Dimensional Mixture Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Zahoransky, Brian; Günther, Tobias; Lawonn, Kai; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    In data science, visual data exploration becomes increasingly more challenging due to the continued rapid increase of data dimensionality and data sizes. To manage complexity, two orthogonal approaches are commonly used in practice: First, data is frequently clustered in high-dimensional space by fitting mixture models composed of normal distributions or Student t-distributions. Second, dimensionality reduction is employed to embed high-dimensional point clouds in a two- or threedimensional space. Those algorithms determine the spatial arrangement in low-dimensional space without further user interaction. This leaves little room for a guided exploration and data analysis. In this paper, we propose a novel visualization system for the effective exploration and construction of potential subspaces onto which mixture models can be projected. The subspaces are spanned linearly via basis vectors, for which a vast number of basis vector combinations is theoretically imaginable. Our system guides the user step-by-step through the selection process by letting users choose one basis vector at a time. To guide the process, multiple choices are pre-visualized at once on a multi-faceted prism. In addition to the qualitative visualization of the distributions, multiple quantitative metrics are calculated by which subspaces can be compared and reordered, including variance, sparsity, and visibility. Further, a bookmarking tool lets users record and compare different basis vector combinations. The usability of the system is evaluated by data scientists and is tested on several high-dimensional data sets.
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    Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Bauer, Ruben; Evers, Marina; Ngo, Quynh Quang; Reina, Guido; Frey, Steffen; Sedlmair, Michael; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Varying the input parameters of simulations or experiments often leads to different classes of results. Parameter sensitivity analysis in this context includes estimating the sensitivity to the individual parameters, that is, to understand which parameters contribute most to changes in output classifications and for which parameter ranges these occur. We propose a novel visual parameter sensitivity analysis approach based on Voronoi cell interfaces between the sample points in the parameter space to tackle the problem. The Voronoi diagram of the sample points in the parameter space is first calculated. We then extract Voronoi cell interfaces which we use to quantify the sensitivity to parameters, considering the class label information of each sample's corresponding output. Multiple visual encodings are then utilized to represent the cell interface transitions and class label distribution, including stacked graphs for local parameter sensitivity. We evaluate the approach's expressiveness and usefulness with case studies for synthetic and real-world datasets.
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    LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Sevastjanova, Rita; Gerling, Robin; Spinner, Thilo; El-Assady, Mennatallah; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Large language models (LLMs) represent words through contextual word embeddings encoding different language properties like semantics and syntax. Understanding these properties is crucial, especially for researchers investigating language model capabilities, employing embeddings for tasks related to text similarity, or evaluating the reasons behind token importance as measured through attribution methods. Applications for embedding exploration frequently involve dimensionality reduction techniques, which reduce high-dimensional vectors to two dimensions used as coordinates in a scatterplot. This data transformation step introduces uncertainty that can be propagated to the visual representation and influence users' interpretation of the data. To communicate such uncertainties, we present LayerFlow - a visual analytics workspace that displays embeddings in an interlinked projection design and communicates the transformation, representation, and interpretation uncertainty. In particular, to hint at potential data distortions and uncertainties, the workspace includes several visual components, such as convex hulls showing 2D and HD clusters, data point pairwise distances, cluster summaries, and projection quality metrics. We show the usability of the presented workspace through replication and expert case studies that highlight the need to communicate uncertainty through multiple visual components and different data perspectives.
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    Beyond Entertainment: An Investigation of Externalization Design in Video Games
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Becker, Franziska; Warnking, Rene Pascal; Brückler, Hendrik; Blascheck, Tanja; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    This article investigates when and how video games enable players to create externalizations in a diverse sample of 388 video games. We follow a grounded-theory approach, extracting externalizations from video games to explore design ideas and relate them to practices in visualization. Video games often engage players in problem-solving activities, like solving a murder mystery or optimizing a strategy, requiring players to interpret heterogeneous data-much like tasks in the visualization domain. In many cases, externalizations can help reduce a user's mental load by making tangible what otherwise only lives in their head, acting as external storage or a visual playground. Over five coding phases, we created a hierarchy of 277 tags to describe the video games in our collection, from which we extracted 169 externalizations. We characterize these externalizations along nine dimensions like mental load, visual encodings, and motivations, resulting in 13 categories divided into four clusters: quick access, storage, sensemaking, and communication. We formulate considerations to guide future work, looking at tasks and challenges, naming potentials for inspiration, and discussing which topics could advance the state of externalization.
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    VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Yan, Xinyuan; Xuan, Xiwei; Ono, Jorge Piazentin; Guo, Jiajing; Mohanty, Vikram; Kumar, Shekar Arvind; Gou, Liang; Wang, Bei; Ren, Liu; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on data slices, which are subsets of the data that share a set of characteristics. Data slice finding automatically identifies conditions or data subgroups where models underperform, aiding developers in mitigating performance issues. Despite its popularity and effectiveness, data slicing for vision model validation faces several challenges. First, data slicing often needs additional image metadata or visual concepts, and falls short in certain computer vision tasks, such as object detection. Second, understanding data slices is a labor-intensive and mentally demanding process that heavily relies on the expert's domain knowledge. Third, data slicing lacks a human-in-the-loop solution that allows experts to form hypothesis and test them interactively. To overcome these limitations and better support the machine learning operations lifecycle, we introduce VISLIX, a novel visual analytics framework that employs state-of-the-art foundation models to help domain experts analyze slices in computer vision models. Our approach does not require image metadata or visual concepts, automatically generates natural language insights, and allows users to test data slice hypothesis interactively. We evaluate VISLIX with an expert study and three use cases, that demonstrate the effectiveness of our tool in providing comprehensive insights for validating object detection models.
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    Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Miller, Jacob; Bhatia, Dhruv; Purchase, Helen; Kobourov, Stephen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    We investigate the usability of Euclidean, spherical and hyperbolic geometries for network visualization. Several techniques have been proposed for both spherical and hyperbolic network visualization tools, based on the fact that some networks admit lower embedding error (distortion) in such non-Euclidean geometries. However, it is not yet known whether a lower embedding error translates to human subject benefits, e.g., better task accuracy or lower task completion time. We design, implement, conduct, and analyze a human subjects study to compare Euclidean, spherical and hyperbolic network visualizations using tasks that span the network task taxonomy. While in some cases accuracy and response times are negatively impacted when using non-Euclidean visualizations, the evaluation shows that differences in accuracy for hyperbolic and spherical visualizations are not statistically significant when compared to Euclidean visualizations. Additionally, differences in response times for spherical visualizations are not statistically significant compared to Euclidean visualizations.
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    Viewpoint Optimization for 3D Graph Drawings
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Wageningen, Simon van; Mchedlidze, Tamara; Telea, Alexandru; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Graph drawings using a node-link metaphor and straight edges are widely used to represent and understand relational data. While such drawings are typically created in 2D, 3D representations have also gained popularity. When exploring 3D drawings, finding viewpoints that help understanding the graph's structure is crucial. Finding good viewpoints also allows using the 3D drawings to generate good 2D graph drawings. In this work, we tackle the problem of automatically finding high-quality viewpoints for 3D graph drawings. We propose and evaluate strategies based on sampling, gradient descent, and evolutionary-inspired meta-heuristics. Our results show that most strategies quickly converge to high-quality viewpoints within a few dozen function evaluations, with meta-heuristic approaches showing robust performance regardless of the quality metric.
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    Enhancing Material Boundary Visualizations in 2D Unsteady Flow through Local Reference Frame Transformations
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Zhang, Xingdi; Rautek, Peter; Theußl, Thomas; Hadwiger, Markus; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    We present a novel technique for the extraction, visualization, and analysis of material boundaries and Lagrangian coherent structures (LCS) in 2D unsteady flow fields relative to local reference frame transformations. In addition to the input flow field, we leverage existing methods for computing reference frames adapted to local fluid features, in particular those that minimize the observed time derivative. Although, by definition, transforming objective tensor fields between reference frames does not change the tensor field, we show that transforming objective tensors, such as the finite-time Lyapunov exponent (FTLE) or Lagrangian-averaged vorticity deviation (LAVD), or the second-order rate-of-strain tensor, into local reference frames that are naturally adapted to coherent fluid structures has several advantages: (1) The transformed fields enable analyzing LCS in space-time visualizations that are adapted to each structure; (2) They facilitate extracting geometric features, such as iso-surfaces and ridge lines, in a straightforward manner with high accuracy. The resulting visualizations are characterized by lower geometric complexity and enhanced topological fidelity. To demonstrate the effectiveness of our technique, we measure geometric complexity and compare it with iso-surfaces extracted in the conventional reference frame. We show that the decreased geometric complexity of the iso-surfaces in the local reference frame, not only leads to improved geometric and topological results, but also to a decrease in computation time.
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    Either Or: Interactive Articles or Videos for Climate Science Communication
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Poehls, Jeran; Meuschke, Monique; Carvalhais, Nuno; Lawonn, Kai; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Effective communication of climate science is critical as climate-related disasters become more frequent and severe. Translating complex information, such as uncertainties in climate model predictions, into formats accessible to diverse audiences is key to informed decision-making and public engagement. This study investigates how different teaching formats can enhance understanding of these uncertainties. This study compares two multimodal strategies: (1) a text-image format with interactive components and (2) an explainer video combining dynamic visuals with narration. Participants' immediate and delayed retention (one week) and engagement are assessed to determine which format offers greater saliency. Sample analysis (n = 622) displayed equivalent retention by viewers between both formats. Metrics assessing interactivity found no correlation between interactivity and information retention. However, a stark contrast was observed in the time viewers spent engaging with each format. The video format was 29% more efficient with information taught over a period of time vs. the article. Additionally, retention on the video format worsened with age (P = 0.004) while retention on the article format improved with education (P = 0.038). These results align with previous findings in literature.
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    Instructional Comics for Self-Paced Learning of Data Visualization Tools and Concepts
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Boucher, Magdalena; AlKadi, Mashael; Bach, Benjamin; Aigner, Wolfgang; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    In this paper, we introduce instructional comics to explain concepts and routines in data visualization tools. As tools for visual data exploration proliferate, there is a growing need for tailored training and onboarding demonstrating interfaces, concepts, and interactions. Building on recent research in visualization education, we detail our iterative process of designing instructional comics for four different types of instructional content. Through a mixed-method eye-tracking study involving 20 participants, we analyze how people engage with these comics when using a new visualization tool, and validate our design choices. We interpret observed behaviors as unique affordances of instructional comics, supporting their use during tasks and complementing traditional instructional methods like video tutorials and workshops, and formulate six guidelines to inform the design of future instructional comics for visualization.
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    An Interactive Visual Enhancement for Prompted Programmatic Weak Supervision in Text Classification
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Lin, Yiming; Wei, Shuqi; Zhang, Huijie; Qu, Dezhan; Bai, Jinghan; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Programmatic Weak Supervision (PWS) has emerged as a powerful technique for text classification. By aggregating weak labels provided by manually written label functions, it allows training models on large-scale unlabeled data without the need for costly manual annotations. As an improvement, Prompted PWS incorporates pre-trained large language models (LLMs) as part of the label function, replacing programs coded by experts with natural language prompts. This allows for the more accessible expression of complex and ambiguous concepts. However, the existing workflow does not fully utilize the advantages of Prompted PWS, and the annotators have difficulty in effectively converging their ideas to develop high-quality LFs, and lack support during the iterations. To address this issue, this study improves the existing PWS workflow through interactive visualization. We first propose a collaborative LF development workflow between humans and LLMs, where the large language model assists humans in creating a structured development space for exploration and automatically generates prompted LFs based on human selections. Annotators can integrate their knowledge through informed selection and judgment. Then, we present an interactive visual system that supports efficient development, in-depth exploration, and iteration of LFs. Our evaluation, comprising a quantitative evaluation on the benchmark, a case study, and a user study, demonstrates the effectiveness of our approach.
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    FairSpace: An Interactive Visualization System for Constructing Fair Consensus from Many Rankings
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Shrestha, Hilson; Cachel, Kathleen; ALKHATHLAN, MALLAK; Rundensteiner, Elke; Harrison, Lane; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Decisions involving algorithmic rankings affect our lives in many ways, from product recommendations, receiving scholarships, to securing jobs. While tools have been developed for interactively constructing fair consensus rankings from a handful of rankings, addressing the more complex real-world scenario- where diverse opinions are represented by a larger collection of rankings- remains a challenge. In this paper, we address these challenges by reformulating the exploration of rankings as a dimension reduction problem in a system called FairSpace. FairSpace provides new views, including Fair Divergence View and Cluster Views, by juxtaposing fairness metrics of different local and alternative global consensus rankings to aid ranking analysis tasks.We illustrate the effectiveness of FairSpace through a series of use cases, demonstrating via interactive workflows that users are empowered to create local consensuses by grouping rankings similar in their fairness or utility properties, followed by hierarchically aggregating local consensuses into a global consensus through direct manipulation. We discuss how FairSpace opens the possibility for advances in dimension reduction visualization to benefit the research area of supporting fair decision-making in ranking based decision-making contexts. Code, datasets and demo video available at: osf.io/d7cwk
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    Embedded and Situated Visualisation in Mixed Reality to Support Interval Running
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Li, Ang; Perin, Charles; Knibbe, Jarrod; Demartini, Gianluca; Viller, Stephen; Cordeil, Maxime; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    We investigate the use of mixed reality visualisations to help pace tracking for interval running. We introduce three immersive visual designs to support pace tracking. Our designs leverage two properties afforded by mixed reality environments to display information: the space in front of the user and the physical environment to embed pace visualisation. In this paper, we report on the first design exploration and controlled study of mixed reality technology to support pacing tracking during interval running on an outdoor running track. Our results show that mixed reality and immersive visualisation designs for interval training offer a viable option to help runners (a) maintain regular pace, (b) maintain running flow, and (c) reduce mental task load.
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    HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Gadirov, Hamid; Wu, Qi; Bauer, David; Ma, Kwan-Liu; Roerdink, Jos B.T.M.; Frey, Steffen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT's significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.
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    Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Eschner, Johannes; Labadie-Tamayo, Roberto; Zeppelzauer, Matthias; Waldner, Manuela; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Bias in generative Text-to-Image (T2I) models is a known issue, yet systematically analyzing such models' outputs to uncover it remains challenging. We introduce the Visual Bias Explorer (ViBEx) to interactively explore the output space of T2I models to support the discovery of visual bias. ViBEx introduces a novel flexible prompting tree interface in combination with zeroshot bias probing using CLIP for quick and approximate bias exploration. It additionally supports in-depth confirmatory bias analysis through visual inspection of forward, intersectional, and inverse bias queries. ViBEx is model-agnostic and publicly available. In four case study interviews, experts in AI and ethics were able to discover visual biases that have so far not been described in literature.
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    The Geometry of Color in the Light of a Non-Riemannian Space
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Bujack, Roxana; Stark, Emily N.; Turton, Terece L.; Miller, Jonah Maxwell; Rogers, David H.; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    We formalize Schrödinger's definitions of hue, saturation, and lightness, building on the foundational idea from Helmholtz that these perceptual attributes can be derived solely from the perceptual metric. We identify three shortcomings in Schrödinger's approach and propose solutions to them. First, to encompass the Bezold-Brücke effect, we replace the straight-line definition of stimulus quality between a color and black with the geodesic path in perceptual color space. Second, to model diminishing returns in color perception, we employ a non-Riemannian perceptual metric, which introduces a potential ambiguity in defining lightness, but our experiments show that this ambiguity is inconsequential. Third, we provide a geometric definition of the neutral axis as the closest color to black within each equal-lightness surface-a definition feasible only in a non-Riemannian framework. Collectively, our solutions provide the first comprehensive realization of Helmholtz's vision: formal geometric definitions of hue, saturation, and lightness derived entirely from the metric of perceptual similarity, without reliance on external constructs.
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    Benchmarking Visual Language Models on Standardized Visualization Literacy Tests
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Pandey, Saugat; Ottley, Alvitta; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    The increasing integration of Visual Language Models (VLMs) into visualization systems demands a comprehensive understanding of their visual interpretation capabilities and constraints. While existing research has examined individual models, systematic comparisons of VLMs' visualization literacy remain unexplored. We bridge this gap through a rigorous, first-ofits- kind evaluation of four leading VLMs (GPT-4, Claude, Gemini, and Llama) using standardized assessments: the Visualization Literacy Assessment Test (VLAT) and Critical Thinking Assessment for Literacy in Visualizations (CALVI). Our methodology uniquely combines randomized trials with structured prompting techniques to control for order effects and response variability - a critical consideration overlooked in many VLM evaluations. Our analysis reveals that while specific models demonstrate competence in basic chart interpretation (Claude achieving 67.9% accuracy on VLAT), all models exhibit substantial difficulties in identifying misleading visualization elements (maximum 30.0% accuracy on CALVI). We uncover distinct performance patterns: strong capabilities in interpreting conventional charts like line charts (76-96% accuracy) and detecting hierarchical structures (80-100% accuracy), but consistent difficulties with data-dense visualizations involving multiple encodings (bubble charts: 18.6-61.4%) and anomaly detection (25-30% accuracy). Significantly, we observe distinct uncertainty management behavior across models, with Gemini displaying heightened caution (22.5% question omission) compared to others (7-8%). These findings provide crucial insights for the visualization community by establishing reliable VLM evaluation benchmarks, identifying areas where current models fall short, and highlighting the need for targeted improvements in VLM architectures for visualization tasks. To promote reproducibility, encourage further research, and facilitate benchmarking of future VLMs, our complete evaluation framework, including code, prompts, and analysis scripts, is available at https://github.com/washuvis/VisLit-VLM-Eval.
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    Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Patel, Tark; Athawale, Tushar M.; Ouermi, Timbwaoga A. J.; Johnson, Chris R.; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state-of-the-art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state-of-the-art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human-brain HARDI datasets.
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    Optimizing Staircase Motifs in Biofabric Network Layouts
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Bartolomeo, Sara Di; Wallinger, Markus; Nöllenburg, Martin; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Biofabric is a novel method for network visualization, with promising potential to highlight specific network features. Recent studies emphasize the importance of staircase motifs - equivalent to fans or stars in node-link diagrams - within Biofabric. However, to effectively showcase these motifs, we need to formulate specialized layout algorithms. This paper introduces a method to compute optimal layouts for Biofabric, focusing on maximizing staircase formation. We present an Integer Linear Programming (ILP) model for this task and evaluate its performance in terms of scalability and output quality against a leading heuristic method, Degreecending. Our results demonstrate that the ILP approach identifies significantly more, and often longer, staircases compared to Degreecending, albeit with the trade-off of higher computation times. Our supplemental material, including a full copy of the paper, code, and results, is available on osf.io.
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    NODKANT: Exploring Constructive Network Physicalization
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Pahr, Daniel; Bartolomeo, Sara Di; Ehlers, Henry; Filipov, Velitchko Andreev; Stoiber, Christina; Aigner, Wolfgang; Wu, Hsiang-Yun; Raidou, Renata Georgia; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit-NODKANT-for constructing modular node-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, and the third group received a pre-constructed representation. While free construction proved on average more time-consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step-by-step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10-14 days compared to those who were given a pre-constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/.
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    Sca2Gri: Scalable Gridified Scatterplots
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Frey, Steffen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Scatterplots are widely used in exploratory data analysis. Representing data points as glyphs is often crucial for in-depth investigation, but this can lead to significant overlap and visual clutter. Recent post-processing techniques address this issue, but their computational and/or visual scalability is generally limited to thousands of points and unable to effectively deal with large datasets in the order of millions. This paper introduces Sca2Gri (Scalable Gridified Scatterplots), a grid-based post-processing method designed for analysis scenarios where the number of data points substantially exceeds the number of glyphs that can be reasonably displayed. Sca2Gri enables interactive grid generation for large datasets, offering flexible user control of glyph size, maximum displacement for point to cell mapping, and scatterplot focus area. While Sca2Gri's computational complexity scales cubically with the number of cells (which is practically bound to thousands for legible glyph sizes), its complexity is linear with respect to the number of data points, making it highly scalable beyond millions of points.
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    Tasks and Visual Abstractions for 3D Chromatin Representation
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Rychlý, Adam; Byška, Jan; Kozlikova, Barbora; Furmanová, Katarína; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    The spatial organization of chromatin fiber directly influences its function. However, the high visual complexity of chromatin spatial models makes the understanding of the structure extremely challenging. Therefore, genomic researchers still primarily rely on indirect analysis of chromatin through 2D views, missing the advantages that 3D visualization can offer. In this paper, we first analyze the task space of genomic research and identify biological domain tasks that can benefit from dedicated spatial representations. We organize these tasks into four categories: tasks related to structural features, additional meta-data, structural relationships, and comparative tasks. We analyze these tasks in terms of their complexity, co-dependence, and potential benefits of 3D-based solutions. Secondly, we present four newly designed visual representations of chromatin 3D structure, focused on enhancing the understanding of structural features and solving relationships tasks. These include the hierarchical nature of spatial chromatin sub-units, their visual abstractions, spatial interactions, and a cumulative representation of chromatin dynamic behavior. We also include feedback from four domain researchers and discuss future steps necessary to make spatial representations valid and valuable part of genomic research.
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    Multipla: Multiscale Pangenomic Locus Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Brandt, Astrid van den; Ståhlbom, Emilia; Workum, Fredericus Johannes Maria van; Wetering, Huub van de; Lundström, Claes; Smit, Sandra; Vilanova, Anna; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
    Comparing gene organization across genomic sequences reveals insights into evolutionary and functional diversity among different organisms and varieties. Performing this task across many sequences, such as from a pangenome, is challenging because of the scale, the density of information, and the inherent variation. Often, analyses are centered on a genomic region of interest-a locus that might be associated with a trait or contain genes within the same family or biological pathway. Within these regions, researchers examine the conservation of gene order and orientation across organisms and assess sequence similarity, along with other gene content features such as gene size, to find biological variations or potential errors in the data. Automated methods in comparative genomics struggle to identify meaningful patterns due to varying and often unknown features of interest, leaving manual, time-intensive, and scalability-challenged visualization as the primary alternative. To address these challenges, we present a multiscale design for studying gene organization within pangenomes, developed in close collaboration with domain experts. Our tool, MULTIPLA, enables users to explore organization at multiple levels of detail in a decluttered manner through layout abstractions, semantic zooming, and layouts with flexible distance definitions and feature selections, combining the advantages of manual and automated methods used in practice. We evaluate the design of MULTIPLA through two pangenomic use cases and conclude with lessons learned from designing multiscale views for pangenomic locus analysis.