DevLan/vispubs huggingface dataset:
A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance
Authors: Shayan Monadjemi, Yuhan Guo, Kai Xu 0003, Alex Endert, Anamaria Crisan
Published at EuroVis, 2026

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.
A Simple Grid-Maps Pipeline: Restructured, Accelerated and Upgraded
Authors: Wouter Meulemans
Published at EuroVis, 2026

Grid maps – spatially arranged small multiples – are a powerful tool to show complex geospatial data. Meulemans et al. (2020) introduced a pipeline for computing high-quality grid maps that are shaped roughly according to their containing geographic outlines. We present a restructured pipeline, along with various efficiency and quality improvements to each of its steps. These changes reduce the running time from minutes to seconds, while improving the resulting grid map. Our changes include speeding up the mosaic-cartograms algorithm by Cano et al. (2015) and generalizing it to general periodic tilings.
AI4VIS EthiCards: A Card-Based Approach to Exploring Ethics in AI for Data Visualization Applications
Authors: Zezhong Wang 0001, Shan Hao, Sheelagh Carpendale
Published at EuroVis, 2026

AI4VIS EthiCards are a card-based approach designed to facilitate ethical exploration in the growing research of artificial intelligence (AI) for visualization (AI4VIS). The continuously increasing integration of AI and data visualization offers many benefits yet inevitably raises ethical concerns. AI4VIS applications, characterized by their interdisciplinary and multifaceted nature, necessitate that researchers, designers, and practitioners develop ethical awareness and critically reflect on their responsibilities within this domain. The AI4VIS EthiCards, organized into six types, i.e., Goal, AI4VIS Task, Technology, Ethical Principle, People-In-Focus, and Challenge, serve as a framework for discussion and ethical reflection. This card deck is designed for students, educators, and practitioners working with AI and visualization, and can be used as a flexible teaching framework, rapid reference, or self-check tool in small-group design and reflection activities. We refined the deck through five workshops and observed three main outcomes: participants’ speculative yet feasible concepts revealed AI4VIS as a high-potential design space; the deck supported more nuanced ethical awareness; and it functioned as a multi-perspective scaffold and shared vocabulary that enabled people from different backgrounds to reason together across technological, user-centred, and value-oriented dimensions.
Anchor Flow Maps: Efficient Sampling and Approximation of the Entire Flow Map
Authors: Daniel Stelter, Thomas Wilde, Christian Rössl, Holger Theisel
Published at EuroVis, 2026

Flow maps are a fundamental concept for the visualization and analysis of time-dependent flows. A common approach is to apply sampling and reconstruction – which comes with a number of problems: flow maps are high-dimensional functions with locally large gradients which makes approximations very challenging. In general, one must apply large sampling resolutions and more advanced methods to achieve sufficiently good results. Existing methods for efficient, high-quality sampling and approximation restrict to subsets of flow maps, e.g., with fixed start and end times, or at least fixed differences. In this paper, we propose the first method to handle the entire flow map by decreasing the dimensionality of the space-time-time domain. Essentially, it is a framework which splits the high-dimensional flow map into few lower-dimensional functions. This enables to achieve much larger resolutions than traditional sampling with the same memory consumption. We use our method in established applications and show that it can significantly decrease errors in the reconstruction by using these larger resolutions.
ARCOL: Aspect Ratio Constrained Orthogonal Layout
Authors: Zainab Alsuwaykit, Yousef Rajeh, Alexandre Kouyoumdjian, Steve Kieffer, Dominik Engel, Sara Di Bartolomeo, Martin Nöllenburg, Ivan Viola
Published at EuroVis, 2026

Orthogonal graph layout algorithms aim to produce clear, compact, and readable network diagrams by arranging nodes and edges along horizontal and vertical lines, while minimizing bends and crossings. Most existing orthogonal layout methods focus primarily on quality criteria such as area usage, total edge length, and bend minimization. Explicitly controlling the global aspect ratio (AR) of the resulting layout is as of now unexplored. Existing orthogonal layout methods offer no control over the resulting AR and their rigid geometric constraints make adaptation of finished layouts difficult. With the increasing variety of aspect ratios encountered in daily life, from wide monitors to tall mobile devices or fixed-size interface panels, there is a clear need for aspect ratio control in orthogonal layout methods. To tackle this issue, we introduce Aspect Ratio-Constrained Orthogonal Layout (ARCOL). Building upon the Human-like Orthogonal Layout Algorithm (HOLA), we integrate aspect ratio at two different stages: (1) into the stress minimization phase, as a soft constraint, allowing the layout algorithm to gently guide node positions toward a specified target AR, while preserving visual clarity and topological faithfulness; and (2) into the tree reattachment phase, where we modify the cost function to favor placements that improve the AR. We evaluate our approach through quantitative evaluation and a user study, as well as expert interviews. Our evaluations show that ARCOL produces balanced and space efficient orthogonal layouts across diverse aspect ratios.
Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization
Authors: Xuefeng Li, Dieter W. Fellner, Xiaoqi Wang, Han-Wei Shen, Yifan Hu 0001
Published at EuroVis, 2026

Network visualization has traditionally relied on heuristic metrics, such as stress, under the assumption that optimizing them leads to aesthetic and informative layouts. However, no single metric consistently produces the most effective results. A data-driven alternative is to learn from human preferences, where labelers select their favored visualization among multiple layouts of the same graphs. These human-preference labels can then be used to train a generative model that approximates human aesthetic preferences. However, obtaining human labels at scale is costly and time-consuming. As a result, this generative approach has so far been tested only with machine-labeled data. In this paper, we explore the use of large language models (LLMs) and vision models (VMs) as proxies for human judgment. Through a carefully designed user study involving 27 participants, we curated a large set of human preference labels. We used this data both to better understand human preferences and to bootstrap LLM/VM labelers. We show that prompt engineering that combines few-shot examples and diverse input formats, such as image embeddings, significantly improves LLM–human alignment, and additional filtering by the confidence score of the LLM pushes the alignment to human–human levels. Furthermore, we demonstrate that carefully trained VMs can achieve VM-human alignment at a level comparable to that between human labelers. Our results suggest that AI can feasibly serve as a scalable proxy for human labelers.
BloomTree: Dynamic Coloring Techniques for Exploring Deep and Wide Tree Structures
Authors: A. Tanaka, Ken Wakita
Published at EuroVis, 2026

This study introduces BloomTree, an interactive Sunburst system for visualizing massive hierarchical datasets, such as the Tree of Life. Traditional static color schemes fail to maintain perceptual distinguishability when applied to millions of nodes, and managing the full tree in memory is computationally costly. BloomTree addresses these challenges through a combined strategy, centered on dynamic, view-dependent, topology-aware color allocation. This technique recomputes color assignments for the visible subtree after each navigation step. This ensures closely related regions appear with coherently related hues and adjacent sectors maintain sufficient contrast. Furthermore, to preserve the user’s mental map during navigation, zooming operations are smoothly animated by simultaneously interpolating sector geometry and color assignments. For computational scalability, BloomTree uses selective subtree streaming and metanode aggregation to handle massive data, storing the full tree on a backend server and streaming only a truncated visible region. We evaluate BloomTree at the system and user levels. System analysis shows that selective streaming and metanode aggregation significantly improve computational scalability, reducing communication volume to about 1/70 and rendering cost to about 1/2,000. A controlled user study confirms that dynamic coloring yields faster responses and higher accuracy for several perceptual and structural tasks, highlighting the effectiveness of the system for exploring multi-million-node trees.
Can LLMs Simulate Target Users in Visualization Case Studies?
Authors: Jena Satkunarajan, Moataz Abdelaal, Steffen Koch 0001, Kuno Kurzhals, Daniel Weiskopf
Published at EuroVis, 2026

Case studies are central to evaluating visualization research as they provide evidence for how the developed approaches support real users, real analytical work, and real data. Conducting such studies can be challenging, since target users with relevant domain expertise are often scarce or even entirely unavailable, while the visualization researchers may lack the required expertise to perform the evaluation themselves. Current research on Large Language Models (LLMs) has shown their strong reasoning ability and usefulness in domain-specific downstream tasks, provoking the question: to what degree and with what capacity can LLMs fill the role of the target users in visualization case studies? We investigate this question by evaluating how LLMs can participate across different phases of visualization case studies. We propose a conceptual framework that explores the different levels of LLM involvement and potential roles when simulating target users. To sketch where the LLM substitution is most plausible, we embed our suggested integration of LLMs within the nested model for visualization theory and design. For evaluation, we replicate case studies from published and unpublished visualization research using multiple state-of-the-art LLMs and compare the model-generated insights to those reported in the papers. Our results show that LLMs can often generate plausible and sometimes even novel interpretations of visual patterns when used for result analysis and validation, but can also struggle when highly contextual, domain-specific knowledge is required.
Class Angular Distortion Index for Dimensionality Reduction
Authors: Kaviru Gunaratne, Stephen G. Kobourov, Jacob Miller 0001
Published at EuroVis, 2026

Dimensionality reduction (DR) techniques are often characterized by whether they preserve global, high-level structures in the data or local, neighborhood structures. This distinction matters in visualization: global methods can obscure clusters while local methods can over-emphasize them. Yet, even when clusters appear distinct, their relative arrangement in the projection may be arbitrary or misleading, a common issue in techniques such as t-SNE and UMAP. Existing cluster quality metrics either only measure cluster separability or assume spherical, globular clusters in the original space. We introduce the Class Angular Distortion Index (CADI), a metric that uses internal angles among point triples to determine the faithfulness of cluster organization in a projection. We show cases on both real and synthetic data where existing cluster metrics fail, but CADI provides an interpretable result. Since it relies on computing angles, CADI is also differentiable, enabling optimization. We demonstrate this with a CADI-based DR technique.
CLUSTERIX: A Hybrid Visualization Model for Hierarchically Clustered Networks
Authors: Carla Binucci, Annika Bonerath, Walter Didimo, Henry Förster, Seok‐Hee Hong, Maria Eleni Pavlidi, Alessandra Tappini
Published at EuroVis, 2026

We introduce CLUSTERIX, a novel hybrid visualization model for representing hierarchically clustered networks, which also supports directed and weighted edges. CLUSTERIX offers an integrated view of both the network and its full cluster hierarchy by compactly visualizing the cluster inclusion tree enriched with links of the network. This is achieved through matrix-based representations at various hierarchy levels, combined with a node-link style linear layout at the leaf level. To support layout computation based on CLUSTERIX, we propose two algorithmic approaches: an exact Integer Linear Program and a fast heuristic, both aimed at minimizing edge crossings. We present an extensive experimental comparison of these algorithmic approaches to highlight the trade-offs between efficiency and effectiveness. Moreover, as a proof of concept for our model, we developed an interactive visualization system based on CLUSTERIX and evaluated its performance through case studies and qualitative feedback from experts in different application domains.
Croissant Charts: Modulating the Performance of Normal Distribution Visualizations with Affordances
Authors: Racquel Fygenson, Enrico Bertini, Lace M. K. Padilla
Published at EuroVis, 2026

Affordances, originating in psychology, describe how an object’s design influences the physical and cognitive actions users may take. Past work applied affordance theory to visualization to explain how design decisions can impact the cognitive actions of visualization readers. In this work, we demonstrate that affordances can complement effectiveness rankings by further explaining the root causes behind visualizations’ task performance. To do so, we conduct a case study on static normal probability density function plots, identifying their current affordances. Next, we identify the optimal affordances for a common probability-comparison task and develop a novel affordance-driven visualization, the Croissant Chart, to support them. We empirically validate the design’s effectiveness through a preregistered study (n = 808), demonstrating how affordances can inform predictable changes in task performance. Our findings underscore the potential for affordance-based approaches to enhance visualization effectiveness and inform future design decisions.
Designing Annotations in Visualization: Considerations from Visualization Practitioners and Educators
Authors: Md Dilshadur Rahman, Devin Lange, Ghulam Jilani Quadri, Paul Rosen 0001
Published at EuroVis, 2026

Annotation is a central mechanism in visualization design that enables people to communicate key insights. Prior research has provided essential accounts of the visual forms annotations take, but less attention has been paid to the decisions behind them. This paper examines how annotations are designed in practice and how educators reflect on those practices. We conducted a two-phase qualitative study: interviews with ten practitioners from diverse backgrounds revealed the heuristics they draw on when creating annotations, and interviews with seven visualization educators offered complementary perspectives situated within broader concerns of clarity, guidance, and viewer agency. These studies provide a systematic account of annotation design knowledge in professional settings, highlighting the considerations, trade-offs, and contextual judgments that shape the use of annotations. By making this tacit expertise explicit, our work complements prior form-focused studies, strengthens understanding of annotation as a design activity, and points to opportunities for improved tool and guideline support.
Do Graph Drawing Aesthetics Matter for AI? A Replication of Foundational Studies in Graph Readability
Authors: Sara Di Bartolomeo, Johann Sebastian Schicho, Aurora Traversini, Simon Dominik Fink, Walter Didimo, Fabrizio Montecchiani
Published at EuroVis, 2026

Graph drawing aesthetics have traditionally been optimized for human readers, leading to well-established principles such as reducing edge crossings, enhancing symmetry, and minimizing bends. These criteria shape layout algorithms and define what “readability” means in network visualization. Today, however, visualizations are increasingly interpreted not only by humans but also by Large Language Models, which now routinely process scientific papers, blog posts, and figures found online and in the wild. This raises a fundamental question: do the same aesthetic criteria that benefit humans also support AI-based visual understanding? Exploring these criteria would inform us, visualization designers and researchers, on how to create network visualizations that are fit for both human and AI readers, and, in turn, enable AI to navigate visualizations in the wild properly. In order to study these criteria, we replicated two foundational studies that established graph drawing aesthetics for humans—only, in our case, the “participant” is AI, and not human. Our findings indicate that AI and human preferences for network visualization are not necessarily the same. We offer a first glance at AI-specific readability criteria, and point toward visualization guidelines that support both human and machine interpretation.
Does Visual Working Memory Decline Explain Age Differences in Visualization Performance?
Authors: Yiran Li 0005, Shan Shao, Peter Baudains, Andrew Isaac Meso, Alfie Abdul-Rahman, Rita Borgo
Published at EuroVis, 2026

Visualization studies that examine age effects on task performance often attribute the performance gap between young adults (YA) and people in late adulthood (PLA) to declines in cognitive abilities, especially reduced visual working memory (VWM) capacity. However, the tasks and visualizations used in prior work typically involve many additional perceptual and cognitive processes, making it difficult to isolate the contribution of VWM decline to age differences in visualization performance. Here, we used a glyph-based discrimination paradigm in which set size (number of glyphs) was manipulated to vary VWM demand systematically. Young adults (YA), mid-age adults (MA), and PLA completed a two-alternative forced-choice task in which they judged which of two glyph-based maps presented a higher overall risk level encoded by the glyphs. For each participant, we fitted psychometric functions to obtain accuracy and precision, and analyzed the response time. The results show that set size impaired performance similarly across age groups, and the marked accuracy drop at the largest set size indicated a practical upper limit on meaningful set size for this task. Contrary to expectations based on cognitive aging, PLA did not perform worse overall or show disproportionate costs at higher VWM demands. Instead, the MA group showed higher precision and was more strongly influenced by the distribution of individual glyph values across the visualization, raising the possibility that strategic or effortful processing may partly compensate for capacity limits. These findings indicate that, for this glyph-based estimation task, age-related VWM decline alone is unlikely to fully explain age differences in performance. Therefore, our findings offer little evidence for treating reduced VWM demand as the primary design goal for visualizations targeting PLA, in the absence of evidence that age-related VWM limitations substantially constrain task performance.
Engagement vs. Understanding: Comparing Immersive Virtual Reality and Desktop Displays for Climate Data Visualization
Authors: Anjana Arunkumar, J. Liu, Christopher Bryan
Published at EuroVis, 2026

Immersive virtual reality (IVR) is increasingly used for scientific data visualization, with the expectation that greater immersion will enhance both engagement and understanding. However, prior research suggests a potential trade-off: IVR can heighten affective responses while impairing cognitive learning due to increased cognitive load. To examine this tension in abstract scientific visualization, we conducted a between-subjects study (N=84) comparing head-mounted display (HMD) VR with traditional desktop displays as participants explored three climate datasets on an interactive 3D globe. We measured cognitive and affective learning outcomes after each exploration trial and at a two-week follow-up. We additionally logged patterns of visualization exploration during trials, and measured user experience post-study and at two-week follow-up. Results reveal systematic divergences: desktop displays led to significantly higher recall accuracy and promoted more systematic exploration patterns, while HMD-based VR produced stronger short-term increases in climate concern and higher satisfaction ratings. Critically, affective changes largely reverted to baseline at follow-up regardless of modality. We further identify five distinct exploration strategies that emerge differentially across modalities and relate directly to learning outcomes. Overall, our findings highlight how modality choice should align with visualization goals, and offer actionable insights into designing effective scientific visualizations that balance cognitive and affective learning objectives. All data and materials are available at: https://osf.io/24w7s/.
Evaluating Encodings for Bivariate Edges in Adjacency Matrices
Authors: Jorge Acosta-Hernandez, Alexander Lex, Tingying He
Published at EuroVis, 2026

We present the first empirical evaluation of techniques for encoding distributions of quantitative edge values within adjacency matrices. In many real-world networks, edges represent not a single value but a set of measurements. While adjacency matrices preserve structural clarity, their compact cells limit the simultaneous display of multiple values. To address this, we explore edge encodings that represent distributions by two values: a measure of central tendency (mean, median, mode) and a measure of dispersion (standard deviation, variance, IQR). We select four possible encodings for evaluation that prior work has suggested are suitable for the limited space available in matrices: a bivariate color palette, embedded bar charts, and two overlaid-mark designs mapping the primary attribute to color and the secondary attribute to area or angle. In a preregistered crowdsourced study with 156 participants, we assessed performance of these encodings across eight analytical tasks and collected readability and aesthetic ratings. Results reveal clear performance regimes: area-based overlaid marks and bar charts achieved the highest overall performance; angle-based marks show moderate but less stable performance, and bivariate color consistently underperforms these alternatives. These findings clarify how visual channels behave under strict constraints and delineate the strengths and limitations of key design choices for multivariate edge visualization.