DevLan/vispubs huggingface dataset:
Evaluating the Perceptual Space of Surface Noise for Visualization
Authors: Anna Sterzik, Niklas Merk, Kai Lawonn
Published at EuroVis, 2026

Surface noise provides a geometric alternative to color for encoding scalar information on surfaces, yet its perceptual characteristics remain insufficiently understood. We present a systematic investigation of how variations in noise amplitude and frequency are perceived when applied to 3D surfaces. Across three online perceptual studies with 142 participants, we gathered similarity judgments for surface noise stimuli and modeled the resulting perceptual space using multi-dimensional scaling (MDS). Our analysis shows that the amplitude–frequency perceptual space requires at least three dimensions for accurate reconstruction; however, the stimuli lie near a 2D manifold. Building on these findings, we derive locally perceptually uniform reparameterizations for both amplitude and frequency, improving the suitability of surface noise as a mapping for scalar data. These results provide perceptual guidance for the design of geometric encodings based on surface perturbation.
Geometry-Aware Alignment and Comparison of Hierarchical Morse Complexes with Applications
Authors: Aniketh Venkat, Attila Gyulassy, Peer‐Timo Bremer, Valerio Pascucci
Published at EuroVis, 2026

Scalar fields derived from 3D X-ray CT scans of samples undergoing ex situ processes, such as thermal aging, chemical etching, or mechanical stress, pose unique challenges for characterizing similarities and differences across acquisitions. Typically, a sample A (source) is imaged, removed, and subjected to experimental conditions that alter its microstructure, and then re-imaged as sample B (target) to study the resulting changes. Direct comparison between A and B is rendered impractical if not impossible for current techniques because the challenges of physical and morphological changes are compounded by the effects of geometric misalignment, differences in reconstruction parameters, discretization artifacts, and changes in acquisition settings such as position, beam intensity, or exposure time. To overcome these challenges, we introduce a geometry-rich topological representation that uses the hierarchical Morse complex to capture the structural relationships among regions segmented within each sample and shape descriptors to characterize their metric properties. With this data structure, we cast the similarity problem as a sequence of optimizations, each minimizing differences in structure and geometry at a given resolution. The sequence of optimizations begins by aligning the fine-scale segmentations of the two samples. Following optimization minimizes differences across incrementally coarser levels, producing a fully synchronized hierarchical representation of the two samples. In addition, we introduce a visualization framework that enables interactive exploration and manual editing of the matched hierarchies, thereby allowing an expert user to further improve the quality of the comparison. We apply our workflow to characterize changes in grain structure for energetic materials undergoing aging, match segmentations for materials under different stress conditions, and perform image registration that outperforms state-of-the-art techniques.
GEVIS: A Workflow-Driven Visual Analytics Approach to Differential Gene Expression Analysis
Authors: Graziano Blasilli, Francesco Fortunato, Cristian Santaroni, GIULIA FISCON, Giuseppe Santucci
Published at EuroVis, 2026

Differential gene expression (DGE) analysis is one of the most widely used techniques for investigating RNA-seq data and supports numerous medical and biological applications, including biomarker identification for diagnosis and prognosis, as well as the evaluation of medical treatments. However, performing DGE analysis typically requires navigating a complex multi-step pipeline and proficiency in programming languages such as R. This poses a barrier for researchers — including biologists and clinicians — who may lack coding expertise, and adds overhead for experienced bioinformaticians.
To address these challenges, we propose a workflow-driven visual analytics approach for DGE analysis that integrates state-of-the-art methodologies and supports interactive exploration of gene expression data through a guided step-by-step process. Building on this workflow, we developed GEVIS, a visual analytics system that enables users to conduct DGE analysis without writing code, thereby reducing analytical overhead and making the process more accessible to a broader audience. Both the workflow and the GEVIS system have been validated by experts in bioinformatics and demonstrated through a use case.
GraphHeatY: Graph-Centered Visual Analysis for Building Design
Authors: Andreas Walch, Attila Szabó, Milena Vuckovic, Eduard Groeller, Johanna Schmidt
Published at EuroVis, 2026

We introduce GraphHeatY, a Visual Analytics approach for exploring and comparing heat transfer behavior in buildings. A building can be conceptualized as a connected graph, where nodes represent rooms or external areas, and edges represent the building elements that connect them. Heat flows along these connections. We employ node-link diagrams and glyph overlays to emphasize the connectivity within buildings. Donut glyphs and bar charts support analysis at both global (i.e., building) and local (i.e., room) levels. GraphHeatY integrates spatial and non-spatial visualizations to support detailed inspection of heat flows while preserving an overview. Further, GraphHeatY allows users to interactively explore differences in insulation, material composition, and system settings, and assess their impact on the building’s thermal state. User evaluations confirmed that GraphHeatY enhances comparative reasoning in early-stage building design.
Guardrail Selection in Line Charts to Contextualize Persuasive Visualizations
Authors: Khandaker Abrar Nadib, Marina Kogan, Alexander Lex, Maxim Lisnic
Published at EuroVis, 2026

Charts used for persuasion can easily veer into being outright misleading when, for instance, cherry-picked data is paired with a deceptive caption, as is commonly encountered on social media. The rise of interactive time-series data explorers for hotly debated topics makes such framing easy to produce and spread. Post-hoc interventions like fact-checking often arrive too late and suffer from persistence of belief. Prior work suggests that guardrails, in the form of contextual comparison lines embedded directly into charts, can reduce these effects. We propose and evaluate a practical set of guardrail sampling strategies for implementing such contextual lines in real systems. In a preregistered mixed-design study with two real-world scenarios (COVID-19 and Stocks), participants viewed persuasive charts with different sets of guardrails and reported trust, estimated rank in the dataset, expressed their perceived completeness of context, as well as subjective preference for different tasks. Across scenarios, guardrails improved trust, accuracy of performance judgments, and perceived completeness of context compared to the control. Taken together, the study offers practical guardrail sampling methods, evidence of their contextual benefits, and insights into participants’ preferences.
HamCat: Ego-Centric Relationship Exploration for Multidimensional Categorical Data
Authors: Hanna Balaka, Helwig Hauser, Laura A. Garrison
Published at EuroVis, 2026

We introduce HamCat, a novel visualization method for exploring and analyzing multidimensional categorical survey data. Typical visualization approaches for multidimensional categorical data do not support simultaneous analysis of attributes and items, nor do they allow for in-depth similarity analysis of an entire dataset from the perspective of a specific reference point. HamCat, in contrast, aims to facilitate detailed analysis of multidimensional categorical data across both attributes and items. Our approach builds on the concept of a Hamming ball combined with a force-directed layout to support ego-centric, user-steered analysis of inter-item and inter-attribute relationships in multidimensional categorical survey data. In addition, our method supports the inclusion and nuanced visualization of missingness. We illustrate the value of HamCat through two case studies. The first case focuses on a survey on wellbeing collected by the European Social Survey, while the second is an expert-driven study for a survey on sense of belonging in computer science higher education. These case studies show how HamCat complements existing analysis workflows to reveal relationships and item groupings across attributes that are not easily discoverable through conventional means. Supplementary materials for our method are available at https://osf.io/uz2jv/.
How Do LLMs See Charts? A Comparative Study on High-Level Visualization Comprehension in Humans and LLMs
Authors: Hyotaek Jeon, Hyunwook Lee, Minjeong Shin, Tapendra Pandey, Joohee Kim, Shinwook Seon, Daeun Jeong, Sungahn Ko, Ghulam Jilani Quadri
Published at EuroVis, 2026

Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to extract complex and interconnected data patterns. Prior perceptual studies of visualization effectiveness have focused on low-level tasks, such as estimating statistical quantities, and have recently explored high-level comprehension of visualization. Despite the growing use of Large Language Models (LLMs) as visualization interpreters, how their interpretations relate to human understanding or what reasoning processes underlie their responses remains insufficiently understood. In this work, we explore LLMs’ comprehension of visualization, examining the alignment between designers’ communicative goals and what their audience sees. We have conducted a qualitative study to investigate the gap between human interpretative strategies and the reasoning pathways of LLMs across three types of visualizations, line graphs, bar graphs, and scatterplots, to identify the high-level patterns generated by LLMs using three prompt conditions. Our analysis results indicate that LLMs exhibit a consistent interpretative strategy that remains unchanged across prompt constraints. Furthermore, we observe two distinct approaches: humans naturally synthesize data into trend-centric narratives, whereas LLMs persist with a structural enumeration of comparisons and numerical ranges. Lastly, we see LLMs achieve visualization comprehension through mechanisms distinct from human intuition, pointing to critical challenges and new opportunities for visualization design.
How Historians Use Visualization: A Corpus-Driven Mixed-Methods Study
Authors: Xinyue Chen, Y. Zhang, Weili Zheng, Chiteng Ma, Xiaoru Yuan
Published at EuroVis, 2026

Visualization in historical research is shifting from isolated attempts to systematic practices. However, data-driven evidence about how historians actually use visualization remains scarce. We present a corpus-driven, mixed-methods study that combines analysis of images from 4,142 research articles across history and digital humanities journals with a collaboratively developed visualization taxonomy and a semi-automatic labeling pipeline. We construct a corpus of 14,021 images, classify 4,831 visualization instances using a hierarchical, domain-informed taxonomy, and analyze patterns of visualization adoption across venues, history subfields, and time. To interpret these patterns, we conduct interviews with 11 historians and use HiFigAtlas system as a boundary object to support joint inspection of the corpus. We identify distinct roles for visualizations in historical research: primary-source, evidence-synthesis, communicative, confirmative, and exploratory. We further find that while historians pursue diverse goals with figures, persistent epistemological and practical barriers, such as uncertainty, provenance, justification burden, and publication constraints, impede the adoption of visualization. This work contributes a grounded account of visualization use in historical scholarship and points to opportunities to better support domain-specific needs.
INFICOND: Investigating Interactive No-code Fine-tuning with Concept-based Knowledge Distillation
Authors: Jinbin Huang, Wenbin He, Liang Gou, Liu Ren, Christopher Bryan
Published at EuroVis, 2026

Knowledge distillation is a widely used technique whereby knowledge from large pre-trained models is transferred into smaller student models. However, this process is non-trivial and traditionally requires technical and theoretical expertise in AI/ML. We investigate a visualization-driven strategy for making this process more accessible and intuitive via developing INFICOND, a novel tool that leverages visual concepts to scaffold the knowledge distillation process and support subsequent no-code fine-tuning of student models. INFICOND’s backend pipeline extracts text-aligned visual concepts and constructs highly interpretable student models; its frontend supports interactively fine-tuning these student models by directly manipulating concept influences. Empirical evaluations help validate that INFICOND effectively supports knowledge distillation and subsequent fine-tuning workflows. We additionally discuss insights and lessons learned about how human-in-the-loop and visualization-driven approaches like INFICOND can support accessible and adaptable AI explainability and model distillation.
Input Visualizations to Track Health Data by Older Adults with Multiple Chronic Conditions
Authors: Shri Harini Ramesh, Foroozan Daneshzand, Matteo Sotelo, Mahsa Sinaei, Fateme Rajabiyazdi
Published at EuroVis, 2026

Older adults living with multiple chronic conditions (MCC) can considerably benefit from collecting and reflecting on their health data. Many older adults collect their health data using various approaches, such as digital tools or handwritten notebooks. However, in these approaches, the act of collecting data does not itself yield insights; sensemaking and reflection happen only if individuals later review their accumulated records. The daily process of data collection thus offers limited opportunity for individuals to actively engage with their data or find the process personally meaningful and enjoyable. Personal data input visualizations using physical tokens offer a promising solution that can help individuals recognize evolving patterns while collecting data and discover meaningful insights in a more serendipitous and engaging manner. Yet, there is a limited understanding of whether and how older adults living with MCC might adopt physical input visualizations to collect data and reflect on their health, and how the tangible, expressive, and personalizable nature of this process supports their sensemaking and reflection. In this paper, we present the results of our interview and diary studies in which older adults living with MCC inputted health data using physical tokens over two weeks. Our findings highlight the diverse and unique needs of older adults for tracking personal health data, illustrating how they adapt strategies and personalize physical input visualizations to align with their individual needs. We demonstrate how older adults integrated input visualizations into daily routines and leveraged tangible markers to reflect on patterns and behaviors, while enjoying the process of tracking and focusing on personal expression and meaningful reflection. Finally, we provide design considerations for supporting older adults with MCC when inputting health data through physical tokens.
Investigating Performance and Practices with Univariate Distribution Charts
Authors: Laura Lotteraner, A. Kurtenkova, Torsten Möller, Daniel Pahr
Published at EuroVis, 2026

A range of charts with different strengths and weaknesses exists to support the visual analysis of univariate distributions, with a limited understanding of which charts best support which tasks and users, and how practitioners use charts. We categorize the available charts for univariate distributions into four groups and present the results of a mixed-methods comparison (n=215) of participants’ perception and preferences across boxplots, violinplots, jittered stripplots, and histograms as representatives of their respective categories. The click-to-select approach in our study, combined with data on participants’ subjective experiences and preferences, allows to both measure accuracy on benchmark tasks and discuss participants’ choices qualitatively. Our analysis reveals differences between charts in task accuracy, common misunderstandings, and preferences across various low-level tasks, and indicates that chart preference and familiarity do not necessarily align with participants’ task performance. Interviews with five visualization practitioners further reveal that charts widely preferred by general audiences (such as histograms) or commonly used in scientific domains (such as boxplots) are not inherently the most effective for all tasks.
Multi2-Vis: Guiding Interactive Exploration Across Temporal Scales in Dynamic Graphs
Authors: Jinghan Bai, Hao Geng, Huijie Zhang, Yiming Lin, Qiushi Xia
Published at EuroVis, 2026

Selecting appropriate temporal intervals for analyzing dynamic graphs is a critical but non-trivial task. Poorly chosen intervals can obscure key structural changes and lead to flawed interpretations, while existing tools often lack effective guidance, forcing users into a cycle of tedious manual adjustments and trial-and-error. We present Multi2-Vis, a visual analytics system that reframes temporal segmentation as a human-in-the-loop interactive refinement process. Multi2-Vis employs an Evaluate-Recommend-Refine loop: it uses structure-aware metrics to automatically identify suboptimal segments and then presents optimized alternatives in a multi-scale visual interface, guiding users toward informed decisions. Through two case studies, a quantitative experiment and a controlled user study, we demonstrate that our guided workflow significantly improves analysts’ analytical efficiency and conclusion quality compared to traditional methods. By transforming temporal segmentation from a rigid prerequisite into a flexible, interactive dialogue, Multi2-Vis provides a more interpretable solution for dynamic graph analysis.
Noisy Graph Patterns via Ordered Matrices
Authors: Jules Wulms, Wouter Meulemans, Bettina Speckmann
Published at EuroVis, 2026

The high-level structure of a graph is a crucial ingredient for the analysis and visualization of relational data. However, discovering the salient graph patterns that form this structure is notoriously difficult for two reasons. (1) Finding important patterns, such as cliques and bicliques, is computationally hard. (2) Real-world graphs contain noise, and therefore do not always exhibit patterns in their pure form. Defining meaningful noisy patterns and detecting them efficiently is a currently unsolved challenge. In this paper, we propose to use well-ordered matrices as a tool to both define and effectively detect noisy patterns. Specifically, we represent a graph as its adjacency matrix and optimally order it using Moran’s I. Standard graph patterns (cliques, bicliques, and stars) now translate to rectangular submatrices. Using Moran’s I, we define a permitted level of noise for such patterns. A combination of exact algorithms and heuristics allows us to efficiently decompose the matrix into noisy patterns. We also introduce a novel motif simplification that visualizes noisy patterns while explicitly encoding the level of noise. We showcase our techniques on several real-world data sets.
Parallel Vectors Extraction using Bézier Clipping
Authors: Nico Daßler, Tobias Günther
Published at EuroVis, 2026

In this paper, we propose a novel local feature extraction algorithm for the parallel vectors (PV) operator. Our method is based on Bézier clipping, which is a bracketing-based root finding method that is commonly-used in computer-aided geometric design. Compared to Bézier bisection, our clipping method exhibits significantly faster convergence and reduces duplicate solutions. Compared to the general Newton-Raphson algorithm, the success of our approach does not depend on the quality of an initial guess. The Bézier-based formulation readily generalizes to polynomial higher-order interpolants, such as for tricubic interpolation. With this, we derive the acceleration in the Sujudi-Haimes vortex coreline criterion directly from the vector field interpolant. In addition, we describe the cross product of two polynomial vector fields in arbitrary polynomial degree, which can be used to approximate non-polynomial interpolants. Further, we examine the effect of Newton refinement on the proposed Bézier clipping method. We evaluate our Bézier clipping method thoroughly on a range of different data sets.
Quantitative Metrics for Edge Bundling of Network Visualizations
Authors: Markus Wallinger, Jacob Miller 0001, Andrei Iulian Maftei, Stephen G. Kobourov
Published at EuroVis, 2026

Edge bundling is widely used for reducing visual clutter in large 2D network and trajectory visualizations. Various edge bundling methods have been proposed, each producing qualitatively distinct outputs for the same data; however, few quantitative metrics exist for systematic evaluation. In this paper, we propose a set of quantitative metrics at the edge level (e.g., geometric properties of the resulting curves), the bundle level (e.g., thickness and number of bundles), and the global level (e.g., ambiguity and clustering). We propose a benchmark of 115 representative datasets and evaluate five representative edge bundling techniques that cover a broad range of methodological approaches. We also conduct a correlation analysis between bundling metrics and network drawing properties. To facilitate further analysis and comparison, we provide an interactive dashboard that includes all methods, metrics, and datasets, enabling side-by-side exploration of edge bundling effects. All supplemental materials and a link to our dashboard application are available at OSF.
Reflections on Traceability for Visualization Research
Authors: Jen Rogers, Derya Akbaba, James Scott-Brown, Alexander Lex, Miriah Meyer
Published at EuroVis, 2026

Decades of advocacy for reproducibility and replication have advanced open, transparent practices in the sciences. However, traditional notions of reproducibility fit poorly with design-oriented visualization research, where insights emerge through subjective, situated, and iterative work. So how can we ensure rigor and transparency in processes that are inherently unreproducible? To introduce transparency in design-oriented research, we propose to focus on traceability: surfacing the origin and development of research contributions based on rich sets of artifacts documenting the design process. We investigated traceability through a collaborative autoethnographic reflection that builds on several years of work exploring ways to make design-oriented research transparent. This exploration includes an experiment to build a tool to support traceability, which we called tRRRacer. The tRRRacer tool provided a testbed for us to operationalize the three tenets of a traceable process: (1) Record abundant, annotated artifacts representative of research activities; (2) Report curated research threads that articulate rationale and evolution of the process, allowing others to (3) Read via interfaces that help retrace claims and assess plausibility. Reflecting on our experiences, we contribute a theorization of traceability and reflections on how we might support it.