44-Issue 3

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https://diglib.eg.org/handle/10.2312/3607144

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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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/.

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