EuroVisShort2025

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https://diglib.eg.org/handle/10.2312/3607192

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Inflecting Data Visualizations for Web-based Scrollytelling
(The Eurographics Association, 2025) Eingartner, Theresa; Drucker, Johanna; Dörk, Marian; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
We propose the idea of inflections as a method for capturing and conveying interpretative interactions with data visualizations. Aiming to narrow the gap between data analysis and story authoring, inflections are subtle modulations of a visualization's graphical attributes that guide attention, highlight important patterns, and embed communicative cues. By parameterizing view changes and annotations in visualizations, authors can embed insights from their data analysis into story segments and connect them with the respective visualization states. This paper presents the general concept and a technical architecture supporting inflectable data visualizations. By leveraging native web technologies and established visualization conventions, we propose a flexible and extensible approach to inflecting visualizations that link exploratory data analysis and data-driven storytelling.
Diminishing Returns in Perceptual Color Space - Now in Color
(The Eurographics Association, 2025) Stark, Emily; Turton, Terece L; Bujack, Roxana; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Recent work has proven the non-Riemannian nature of perceptual color space by identifying the existence of diminishing returns along the luminance axis. A lurking question is if the luminance channel might somehow be the only axis that exhibits diminishing returns. In this short paper, we report on a companion study along two color axes: green to pink and blue to orange. These paths through color space were chosen as the most likely ones to form geodesics based on maximal agreement between color similarity experiments and hue constancy experiments. Our crowdsourced studies confirmed the existence of diminishing returns along both lines of color studied, bolstering the evidence that perceptual color space is non-Riemannian.
Rose Charts: Area or Length Encoding for Fill Level of Circle Sectors?
(The Eurographics Association, 2025) Kiesel, Dora; Riehmann, Patrick; Froehlich, Bernd; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
This paper examines the accuracy of value estimation from the fill level in circle sectors of rose charts, a circular chart type where all sectors share the same angle and encode values through either radius or area. But which encoding yields more accurate estimates? We conducted a user study comparing different sector configurations and chart sizes as well as the estimation errors of rose versus bar charts. Our findings indicate that both area and radius influence estimation accuracy. For values below 65%, area dominates as a visual cue, whereas for larger values, a transition to length perception can be observed. Based on these insights, we propose a transfer function to correct for average estimation errors and provide practical guidelines for the effective use of rose charts.
Monte Carlo Methods for 2D Flow Visualization
(The Eurographics Association, 2025) Tian, Xingze; Günther, Tobias; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
In this paper, we investigate how recent advances from the computer graphics literature can be applied to improve the visualization of two-dimensional vector fields. To this end, we propose two different approaches that both start from a set of evenly-spaced streamlines. The first approach avoids the need for contrast normalization, which is usually required for LIC approaches. For this, the image synthesis is phrased as a diffusion problem by placing double-sided Dirichlet boundary conditions along the streamlines. The diffusion problem is formally modeled as a linear elliptic partial differential equation, which is solved stochastically using a variant of the walk-on-spheres algorithm in order to achieve anti-aliased results. The second approach leverages human's perception of shape to convey flow patterns. For this, we lift the streamlines into the third dimension and generate visual contrast among adjacent streamlines by means of ambient occlusion. To synthesize the images, we apply a physically based material model and employ a Monte Carlo renderer to simulate the light transport.
Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations
(The Eurographics Association, 2025) Chang, Minsuk; Wang, Yao; Wang, Huichen Will; Bulling, Andreas; Bearfield, Cindy Xiong; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewingbased saliency models- although visual attention is inherently task-dependent. Collecting task-relevant importance data remains a resource-intensive challenge. To address this, we introduce Grid Labeling - a novel annotation method for collecting task-specific importance data to enhance saliency prediction models. Grid Labeling dynamically segments visualizations into Adaptive Grids, enabling efficient, low-effort annotation while adapting to visualization structure. We conducted a humansubject study comparing Grid Labeling with existing annotation methods, ImportAnnots, and BubbleView across multiple metrics. Results show that Grid Labeling produces the least noisy data and the highest inter-participant agreement with fewer participants while requiring less physical (e.g., clicks/mouse movements) and cognitive effort. An interactive demo and the accompanying dataset are available at https://github.com/jangsus1/Grid-Labeling.
NFGD: Neighborhood-Faithful Graph Drawing
(The Eurographics Association, 2025) Fan, Yuming; Hong, Seok-Hee; Meidiana, Amyra; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Neighborhood faithfulness metrics measure how faithfully the ground truth neighbors of vertices in a graph G are represented as the geometric neighbors of vertices in a drawing D of G. In this paper, we present NFGD, a post-processing algorithm for optimizing the neighborhood faithfulness of graph drawings. Experiments demonstrate the effectiveness of NFGD for computing neighbor-faithful drawings, on average 320% improvement over the popular graph drawing algorithms: 425% over Stress Majorization (SM) and 215% over force-directed algorithm Fruchterman-Reingold (FR). In particular, for scale-free graphs, NFGD-SM achieves 776% improvement over SM and NFGD-FR obtains 597% improvement over FR.
Transparent Risks Revisited: Evidence for a Dark-is-More Bias in Risk Perception
(The Eurographics Association, 2025) Matzen, Laura E.; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Prior research has shown that different representations of uncertainty in data visualizations can lead to more (or less) riskaverse decision making. It is crucial for researchers to develop a better scientific understanding of these effects so that visualizations such as hazard maps can be designed to support viewers in reasoning about risk and probability. This paper presents a follow-up to a prior study that showed that participants underestimated the risk from a wildfire when transparency was used to represent different risk levels. In the present study, we test the hypothesis that the participants' decisions about risk are influenced by the dark-is-more bias. Across three experiments using the same wildfire evacuation task, we found that participants were consistently more likely to evacuate when the probability bands representing the fire risk were darker.
Riemannian Inhomogeneity and Anisotropy of Perceptual Color Space
(The Eurographics Association, 2025) Molchanov, Vladimir; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
The geometry of perceived color space is widely recognized as non-Euclidean, with the Riemannian framework commonly adopted for its analysis. However, existing evidence, such as the principle of diminishing returns, suggests that the color space may be globally non-Riemannian. In this work, we investigate the local inhomogeneities of the perceived color space under the Riemannian setting. Specifically, we evaluate the local agreement between the Riemannian model and the color-difference function. To achieve this, we perform numerical experiments to assess the accuracy of the parallelogram law, a necessary condition for the local validity of the metric tensor. Furthermore, we introduce several measures of local anisotropy to quantify directional variations in perceived color distances and compute these measures within the chromatic planes of the CIELAB color space. Our findings describe the spatial variation of Riemannian inhomogeneities and distance anisotropy, which can be used to construct adaptive spatial meshes and improve the accuracy of computations in color space. While our techniques are demonstrated on the CIELAB color model with the ΔE2000 metric, they are generalizable to the discretization of arbitrary non-Euclidean metric spaces.
Cross-Linguistic and Cultural Adaptation of the Mini-VLAT: A Study on Visualization Literacy Assessment in Ukrainian
(The Eurographics Association, 2025) Omelchenko, Oleh; Konopatska, Oleksandra; Khomenko, Pavlo; Kharin, Tymofii; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
This study explores the impact of cultural adaptation, beyond simple translation, on visualization literacy assessment. Most assessment tools are developed for English-speaking audiences. We designed and compared three versions of the Mini-VLAT (a shortened, validated visualization literacy test) among Ukrainian speakers: an original English version, a directly translated Ukrainian version, and a culturally adapted Ukrainian version. The adapted version showed significantly higher accuracy compared to the original English version overall. Per-chart analysis revealed that some adapted charts led to statistically significant improvements in assessed metrics compared to both the original and translated versions. These findings demonstrate that cultural adaptation, specifically addressing context-specific knowledge, can enhance the validity and usability of visualization literacy assessments for non-native English speakers with moderate-to-high English proficiency.
Burger Charts: A Quantitative Display of Set Intersections
(The Eurographics Association, 2025) Riehmann, Patrick; Kiesel, Dora; König, Joshua; Froehlich, Bernd; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
We present Burger Charts, a quantitative display of set intersections that stack the contributing sets on top of each other. Sets are represented as horizontal layers. The size of each set intersection is encoded by the width of its layer section, forming a vertically stacked, burger-like visual representation. A visual skewer maintains the unity of the burger by bridging gaps of those set layers that do not contribute to the intersection. The color coding of the sets emphasizes which set contributes to which intersection. We use Burger Charts to visualize and analyze keyword co-occurrences of an argument search engine. They support a quantitative discourse analysis by providing insights into the distribution of sets of keyword occurrences. Users can interactively explore keywords of online discourses on controversial topics, identify prevalent keyword co-occurrences, and even uncover overlooked perspectives.
Seamless Collaborative Coding with Visualization
(The Eurographics Association, 2025) Becker, Franziska; Warnking, Rene Pascal; Blascheck, Tanja; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Coding data is a time-consuming activity, but one that is found in many places like scientific research and public online spaces. We developed the CollaCode system to support collaborative coding for a video game dataset, leveraging visualization with ubiquitous editing for seamless transitions between editing data and analyzing coding results. CollaCode employs multiple visualizations to support different activities in the coding workflow: tagging data, analyzing coding results, and resolving disagreements. Compared to existing approaches, our system explicitly models coding iterations, allowing coders to understand tag provenance while giving more transparency to the coding process. We discuss challenges in collaborative coding settings, how our system design addresses these challenges, and opportunities we see for future work.
Sequence Tube Maps for Structural Variants
(The Eurographics Association, 2025) Brandt, Astrid van den; Geelen, Jasper P. J.; Wetering, Huub van de; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Discovery of large sequence changes-structural variants-is crucial yet challenging in genomics. Pangenome graphs aid in their detection, representing DNA of multiple species as a unified structure. Sequence Tube Maps (STM) visualize these graphs as metro maps but quickly become cluttered. Based on STM, we present SVSTM, which addresses this challenge by reducing visual elements and enabling adjustable detail levels. Overview+detail views highlight large variants, summarize small ones, and offer details on demand. We show SVSTM's utility through use cases and evaluations with domain experts and developers.
DigitalTraces: Unveiling Fraud through Interactive User Behaviour Exploration
(The Eurographics Association, 2025) Narciso, João Bernardo; Feliciano, Beatriz; Costa, Rita; Bizarro, Pedro; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Fraud detection teams in financial institutions face the challenge of identifying suspicious activity within user behaviour. However, existing tools often lack the ability to seamlessly integrate multiple dimensions of digital activity into a single, interactive visualisation, leading to increased cognitive load and preventing analysts from quickly spotting anomalies in varying sources of information. This paper introduces DigitalTraces, a visual analytics tool aimed at improving the detection of fraudulent patterns particularly in the dimensions tied with digital activity. The system combines several stacked timelines to offer an overview of multiple activity dimensions, integrating online banking session data, device identifiers, transactional activities, and account information. We validated our tool with a think-aloud experiment where two fraud analysts were tasked with detecting anomalies in a financial fraud scenario. Experts emphasised the tool's ability to provide intuitive insights and enhance understanding.
VECNA: Visual Exploration, Comparison and Analysis of Reconstructed Spatiotemporal Scientific Simulation Data
(The Eurographics Association, 2025) Mishra, Aditi; Biswas, Ayan; Bryan, Chris; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Data-driven sampling and reconstruction techniques are increasingly being employed in scientific computing applications to aggressively reduce data volumes while retaining the crucial features of spatiotemporal datasets. Such data must be reconstructed for analysis, but it is difficult for domain experts to assess reconstruction quality, particularly given the pace at which new methods are being developed and a lack of support in existing tools. To help address this, we introduce VECNA, a visual analytics system for exploring and comparing reconstructed scientific datasets. Developed through collaboration with high-performance computing researchers, VECNA enables intuitive qualitative and quantitative comparisons among diverse reconstruction methodologies. We validate VECNA via a usage scenario and empirical expert assessments to demonstrate its efficacy in empowering users to discern nuances in reconstruction quality and identify regions of interest within datasets, facilitating more informed subsequent analyses.
Prompt Lenses: Improving the Magic of Lenses (for Text Analysis)
(The Eurographics Association, 2025) Satkunarajan, Jena; Wohlfart, Phillip; Beck, Samuel; Franke, Max; Koch, Steffen; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Incorporating the analytical power of LLMs with the fast-paced interaction of magic lens-based exploration is an intriguing prospect. Unfortunately, the costs of LLM-generated analyses are high, and applying them continuously seems prohibitive at the moment. Accordingly, we suggest an LLM integration into magic lenses that supports the progressive triggering of costly analyses based on users' interest in the data hovered with the lens. We exemplify this approach with a lens technique for exploring dimensionality-reduced embeddings of visualization paper abstracts shown in a scatterplot. Our proposed approach links back analysis results to the explored visualization improving the comprehensibility and the assessment of the shown results.
EuroVis 2025 Short Papers: Frontmatter
(The Eurographics Association, 2025) El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
The Effect of Internal Patterns on Perception Accuracy in Bar Charts
(The Eurographics Association, 2025) Wong, Ukrit; Ruchikachorn, Puripant; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Bar charts are widely used for comparing categorical data due to their simplicity and effectiveness, while pictographs enhance engagement and memory retention. This study aims to combine the strengths of both visualization techniques through redundant encoding, integrating internal patterns to improve perception accuracy. By testing various pattern designs, the study evaluates their impact on value estimation. Results indicate that some patterns enhance accuracy, while others introduce complexity that can hinder readability. These findings contribute to optimizing bar chart design for clearer and more intuitive data interpretation.
Visual Fingerprints of Vibration Signals Using Time Delay Embeddings
(The Eurographics Association, 2025) Rakuschek, Julian; Boesze, Adrian; Schmidt, Johanna; Schreck, Tobias; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Most machines generate vibrations during operation, but effectively visualizing these vibrations is often a challenge, due to large and high-resolution data. Line charts suffer from overplotting, while frequency-domain analysis requires specialized knowledge in signal processing. We introduce a method that bridges the gap between time-domain and frequency-domain analysis: a visual fingerprint computed through the time delay embedding of the vibration data. This fingerprint helps identify segments exhibiting periodic behavior and can be used to cluster similar segments within a vibration signal. Additionally, we demonstrate its practical application in predictive maintenance, showcasing its potential for real-world industrial use.
Visualizing Interaction Effects for Combinatorial Cost-Benefit Analysis
(The Eurographics Association, 2025) Bieg, Till; Krottenberger, Isabella; Knöttner, Sophie; Oppermann, Michael; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Interaction effects occur when the combined impact of multiple actions differs from the sum of their individual effects. This creates challenges for scenarios that require analyzing how different combinations of actions affect an outcome of interest (i.e., combinatorial cost-benefit analysis). Visualization techniques support interpretation, but most existing approaches rely on multiseries line charts (interaction plots), which are widely used but do not explicitly support comparing interaction effects across alternative action sets. Accordingly, we investigate visualization approaches for analyzing interaction effects in combinatorial cost-benefit analysis. We propose a method integrating multi-attribute set rankings with small-scale visualizations to facilitate comparative analysis. Through a user study, we evaluate the effectiveness of three techniques for representing two- and threeway interactions. We present preliminary findings and discuss design implications to inform future visualization research.
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
(The Eurographics Association, 2025) Jeon, Hyeon; Lee, Hyunwook; Kuo, Yun-Hsin; Yang, Taehyun; Archambault, Daniel; Ko, Sungahn; Fujiwara, Takanori; Ma, Kwan-Liu; Seo, Jinwook; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual analytics with DR. Relying on the previous classification of the relevant literature, our guide helps both practitioners to (1) assess their current DR expertise and (2) identify papers that will further enhance their understanding. Interview studies with three experts in DR and data visualizations validate the significance, comprehensiveness, and usefulness of our guide.

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