DevLan/vispubs huggingface dataset:
Cluster-Based Random Forest Visualization and Interpretation
Authors: Max Sondag, Christofer Meinecke, Dennis Collaris, Tatiana von Landesberger, Stef van den Elzen
Published at Vis, 2025

Random forests are a machine learning method used to automatically classify datasets and consist of a multitude of decision trees. While these random forests often have higher performance and generalize better than a single decision tree, they are also harder to interpret. This paper presents a visualization method and system to increase interpretability of random forests. We cluster similar trees which enables users to interpret how the model performs in general without needing to analyze each individual decision tree in detail, or interpret an oversimplified summary of the full forest. To meaningfully cluster the decision trees, we introduce a new distance metric that takes into account both the decision rules as well as the predictions of a pair of decision trees. We also propose two new visualization methods that visualize both clustered and individual decision trees: (1) The Feature Plot, which visualizes the topological position of features in the decision trees, and (2) the Rule Plot, which visualizes the decision rules of the decision trees. We demonstrate the efficacy of our approach through a case study on the “Glass” dataset, which is a relatively complex standard machine learning dataset, as well as a small user study.
Collaborating across Domains and Roles: An Interview Study of Visualization Design Practices
Authors: Yiwen Xing, Maria Teresa Ortoleva, Rita Borgo, Alfie Abdul-Rahman
Published at Vis, 2025

Visualization design study is a widely adopted approach for developing tailored visual solutions to domain-specific problems through close interdisciplinary collaboration. While the visualization community has proposed generalizable frameworks, there is a growing need for domain-aware methodologies that address discipline-specific challenges and refine design study practices. To investigate how domain characteristics and collaborator roles influence the design study process, we conducted interviews with 15 experts, including domain specialists from the humanities, arts, applied sciences, and artificial intelligence, as well as visualization researchers and developers, with direct experience in design studies. Our findings reveal tensions and opportunities that arise from differing expectations, communication styles, and levels of engagement among collaborators at various stages of the design process, including problem formulation, co-design, and evaluation. We highlight how domain-specific norms and role dynamics shape collaboration and influence the trajectory of visualization projects. Based on these insights, we offer practical considerations to help visualization researchers anticipate domain-specific challenges, foster mutual understanding, and adapt their methods accordingly. Our study contributes to ongoing efforts to support more context-sensitive, sustainable, and inclusive design study practices across diverse application domains.
ConceptViz: A Visual Analytics Approach for Exploring Concepts in Large Language Models
Authors: Haoxuan Li, Zhen Wen 0001, Qiqi Jiang, Chenxiao Li, Yuwei Wu, Yuchen Yang, Yiyao Wang, Xiuqi Huang, Minfeng Zhu 0001, Wei Chen 0001
Published at Vis, 2025

Large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks. Understanding how LLMs internally represent knowledge remains a significant challenge. Despite Sparse Autoencoders (SAEs) have emerged as a promising technique for extracting interpretable features from LLMs, SAE features do not inherently align with human-understandable concepts, making their interpretation cumbersome and labor-intensive. To bridge the gap between SAE features and human concepts, we present ConceptViz, a visual analytics system designed for exploring concepts in LLMs. ConceptViz implements a novel Identification ⇒ Interpretation ⇒Validation pipeline, enabling users to query SAEs using concepts of interest, interactively explore concept-to-feature alignments, and validate the correspondences through model behavior verification. We demonstrate the effectiveness of ConceptViz through two usage scenarios and a user study. Our results show that ConceptViz enhances interpretability research by streamlining the discovery and validation of meaningful concept representations in LLMs, ultimately aiding researchers in building more accurate mental models of LLM features. Our code and user guide are publicly available at https://github.com/Happy-Hippo209/conceptViz.
Conch: Competitive Debate Analysis via Visualizing Clash Points and Hierarchical Strategies
Authors: Qianhe Chen, Yong Wang 0021, Yixin Yu 0001, Xiyuan Zhu, Xuerou Yu, Ran Wang 0005
Published at Vis, 2025

In-depth analysis of competitive debates is essential for participants to develop argumentative skills and refine strategies, and further improve their debating performance. However, manual analysis of unstructured and unlabeled textual records of debating is time-consuming and ineffective, as it is challenging to reconstruct contextual semantics and track logical connections from raw data. To address this, we propose Conch, an interactive visualization system that systematically analyzes both what is debated and how it is debated. In particular, we propose a novel parallel spiral visualization that compactly traces the multidimensional evolution of clash points and participant interactions throughout debate process. In addition, we leverage large language models with well-designed prompts to automatically identify critical debate elements such as clash points, disagreements, viewpoints, and strategies, enabling participants to understand the debate context comprehensively. Finally, through two case studies on real-world debates and a carefully-designed user study, we demonstrate Conch's effectiveness and usability for competitive debate analysis.
Correcting Misperceptions at a Glance: Using Data Visualizations to Reduce Political Sectarianism
Authors: Douglas Markant, Subham Sah, Alireza Karduni, Milad Rogha, My T. Thai, Wenwen Dou
Published at Vis, 2025

Political sectarianism is fueled in part by misperceptions of political opponents: People commonly overestimate the support for extreme policies among members of the other party. These misperceptions inflame partisan animosity and may be used to justify extremism among one's own party. Research suggests that correcting partisan misperceptions-by informing people about the actual views of outparty members-may reduce one's own expressed support for political extremism, including partisan violence and antidemocratic actions. However, there remains a limited understanding of how the design of correction interventions drives these effects. The present study investigated how correction effects depend on different representations of outparty views communicated through data visualizations. Building on prior interventions that present the average outparty view, we consider the impact of visualizations that more fully convey the range of views among outparty members. We conducted an experiment with U.S.-based participants from Prolific (N=239 Democrats, N=244 Republicans). Participants made predictions about support for political violence and undemocratic practices among members of their political outparty. They were then presented with data from an earlier survey on the actual views of outparty members. Some participants viewed only the average response (Mean-Only condition), while other groups were shown visual representations of the range of views from 75% of the outparty (Mean+Interval condition) or the full distribution of responses (Mean+Points condition). Compared to a control group that was not informed about outparty views, we observed the strongest correction effects (i.e., lower support for political violence and undemocratic practices) among participants in the Mean-only and Mean+Points condition, while correction effects were weaker in the Mean+Interval condition. In addition, participants who observed the full distribution of out-party views (Mean+Points condition) were most accurate at later recalling the degree of support among the outparty. Our findings suggest that data visualizations can be an important tool for correcting pervasive distortions in beliefs about other groups. However, the way in which variability in outparty views is visualized can significantly shape how people interpret and respond to corrective information. Supplemental materials for this paper are available at this OSF repository.
Critical Design Strategy: a Method for Heuristically Evaluating Visualisation Designs
Authors: Jonathan C. Roberts, Hanan Alnjar, Aron E. Owen, Panagiotis D. Ritsos
Published at Vis, 2025

We present the Critical Design Strategy (CDS)-a structured method designed to facilitate the examination of visualisation designs through reflection and critical thought. The CDS helps designers think critically and make informed improvements using heuristic evaluation. When developing a visual tool or pioneering a novel visualisation approach, identifying areas for enhancement can be challenging. Critical thinking is particularly crucial for visualisation designers and tool developers, especially those new to the field, such as studying visualisation in higher education. The CDS consists of three stages across six perspectives: Stage 1 captures the essence of the idea by assigning an indicative title and selecting five adjectives (from twenty options) to form initial impressions of the design. Stage 2 involves an in-depth critique using 30 heuristic questions spanning six key perspectives-user, environment, interface, components, design, and visual marks. Stage 3 focuses on synthesising insights, reflecting on design decisions, and determining the next steps forward. We introduce the CDS and explore its use across three visualisation modules in both undergraduate and postgraduate courses. Our longstanding experience with the CDS has allowed us to refine and develop it over time: from its initial creation through workshops in 2017/18 to improvements in wording and the development of two applications by 2020, followed by the expansion of support notes and refinement of heuristics through 2023; while using it in our teaching each year. This sustained use allows us to reflect on its practical application and offer guidance on how others can incorporate it into their own work.
CrossSet: Unveiling the Complex Interplay of Two Set-typed Dimensions in Multivariate Data
Authors: Kresimir Matkovic, Rainer Splechtna, Denis Gracanin, Helwig Hauser
Published at Vis, 2025

The interactive visual analysis of set-typed data, i.e., data with attributes that are of type set, is a rewarding area of research and applications. Valuable prior work has contributed solutions that enable the study of such data with individual set-typed dimensions. In this paper, we present CrossSet, a novel method for the joint study of two set-typed dimensions and their interplay. Based on a task analysis, we describe a new, multi-scale approach to the interactive visual exploration and analysis of such data. Two set-typed data dimensions are jointly visualized using a hierarchical matrix layout, enabling the analysis of the interactions between two set-typed attributes at several levels, in addition to the analysis of individual such dimensions. CrossSet is anchored at a compact, large-scale overview that is complemented by drill-down opportunities to study the relations between and within the set-typed dimensions, enabling an interactive visual multi-scale exploration and analysis of bivariate set-typed data. Such an interactive approach makes it possible to study single set-typed dimensions in detail, to gain an overview of the interaction and association between two such dimensions, to refine one of the dimensions to gain additional details at several levels, and to drill down to the specific interactions of individual set-elements from the set-typed dimensions. To demonstrate the effectiveness and efficiency of CrossSet, we have evaluated the new method in the context of several application scenarios.
Data Augmentation for Visualization Design Knowledge Bases
Authors: Hyeok Kim, Jeffrey Heer
Published at Vis, 2025

Visualization knowledge bases enable computational reasoning and recommendation over a visualization design space. These systems evaluate design trade-offs using numeric weights assigned to different features (e.g., binning a variable). Feature weights can be learned automatically by fitting a model to a collection of chart pairs, in which one chart is deemed preferable to the other. To date, labeled chart pairs have been drawn from published empirical research results; however, such pairs are not comprehensive, resulting in a training corpus that lacks many design variants and fails to systematically assess potential trade-offs. To improve knowledge base coverage and accuracy, we contribute data augmentation techniques for generating and labeling chart pairs. We present methods to generate novel chart pairs based on design permutations and by identifying under-assessed features-leading to an expanded corpus with thousands of new chart pairs, now in need of labels. Accordingly, we next compare varied methods to scale labeling efforts to annotate chart pairs, in order to learn updated feature weights. We evaluate our methods in the context of the Draco knowledge base, demonstrating improvements to both feature coverage and chart recommendation performance.
Data Speaks, But Who Gives It a Voice? Understanding Persuasive Strategies in Data-Driven News Articles
Authors: Zikai Li, Chuyi Zheng, Ziang Li, Yang Shi 0007
Published at Vis, 2025

Data-driven news articles combine narrative storytelling with data visualizations to inform and influence public opinion on pressing societal issues. These articles often employ persuasive strategies, which are rhetorical techniques in narrative framing, visual rhetoric, or data presentation, to influence audience interpretation and opinion formation regarding information communication. While previous research has examined whether and when data visualizations persuade, the strategic choices made by persuaders remain largely unexplored. Addressing this gap, our work presents a taxonomy of persuasive strategies grounded in psychological theories and expert insights, categorizing 15 strategies across five dimensions: Credibility, Guided Interpretation, Reference-based Framing, Emotional Appeal, and Participation Invitation. To facilitate large-scale analysis, we curated a dataset of 936 data-driven news articles annotated with both persuasive strategies and their perceived effects. Leveraging this corpus, we developed a multimodal, multi-task learning model that jointly predicts the presence of persuasive strategies and their persuasive effects by incorporating both embedded (text and visualization) and explicit (visual narrative and psycholinguistic) features. Our evaluation demonstrates that our model outperforms state-of-the-art baselines in identifying persuasive strategies and measuring their effects.
Dataset-Adaptive Dimensionality Reduction
Authors: Hyeon Jeon, Jeongin Park, Soohyun Lee, Dae Hyun Kim 0005, Sungbok Shin, Jinwook Seo
Published at Vis, 2025

Selecting the appropriate dimensionality reduction (DR) technique and determining its optimal hyperparameter settings that maximize the accuracy of the output projections typically involves extensive trial and error, often resulting in unnecessary computational overhead. To address this challenge, we propose a dataset-adaptive approach to DR optimization guided by structural complexity metrics. These metrics quantify the intrinsic complexity of a dataset, predicting whether higher-dimensional spaces are necessary to represent it accurately. Since complex datasets are often inaccurately represented in two-dimensional projections, leveraging these metrics enables us to predict the maximum achievable accuracy of DR techniques for a given dataset, eliminating redundant trials in optimizing DR. We introduce the design and theoretical foundations of these structural complexity metrics. We quantitatively verify that our metrics effectively approximate the ground truth complexity of datasets and confirm their suitability for guiding dataset-adaptive DR workflow. Finally, we empirically show that our dataset-adaptive workflow significantly enhances the efficiency of DR optimization without compromising accuracy.
DataWink: Reusing and Adapting SVG-based Visualization Examples with Large Multimodal Models
Authors: Liwenhan Xie, Yanna Lin, Can Liu 0004, Huamin Qu, Xinhuan Shu
Published at Vis, 2025

Creating aesthetically pleasing data visualizations remains challenging for users without design expertise or familiarity with visualization tools. To address this gap, we present DataWink, a system that enables users to create custom visualizations by adapting high-quality examples. Our approach combines large multimodal models (LMMs) to extract data encoding from existing SVG-based visualization examples, featuring an intermediate representation of visualizations that bridges primitive SVG and visualization programs. Users may express adaptation goals to a conversational agent and control the visual appearance through widgets generated on demand. With an interactive interface, users can modify both data mappings and visual design elements while maintaining the original visualization's aesthetic quality. To evaluate DataWink, we conduct a user study (N=12) with replication and free-form exploration tasks. As a result, DataWink is recognized for its learnability and effectiveness in personalized authoring tasks. Our results demonstrate the potential of example-driven approaches for democratizing visualization creation.
Deconstructing Implicit Beliefs in Visual Data Journalism: Unstable Meanings Behind Data as Truth & Design for Insight
Authors: Ke Er Amy Zhang, Jodie Jenkinson, Laura A. Garrison
Published at Vis, 2025

We conduct a deconstructive reading of a qualitative interview study with 17 visual data journalists from newsrooms across the globe. We borrow a deconstruction approach from literary critique to explore the instability of meaning in language and reveal implicit beliefs in words and ideas. Through our analysis we surface two sets of opposing implicit beliefs in visual data journalism: objectivity/subjectivity and humanism/mechanism. We contextualize these beliefs through a genealogical analysis, which brings deconstruction theory into practice by providing a historic backdrop for these opposing perspectives. Our analysis shows that these beliefs held within visual data journalism are not self-enclosed but rather a product of external societal forces and paradigm shifts over time. Through this work, we demonstrate how thinking with critical theories such as deconstruction and genealogy can reframe “success” in visual data storytelling and diversify visualization research outcomes. These efforts push the ways in which we as researchers produce domain knowledge to examine the sociotechnical issues of today's values towards datafication and data visualization. All supplemental materials for this work are available at osf.io/5fr48.
DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning
Authors: Zhihao Shuai, Boyan Li 0001, Siyu Yan, Yuyu Luo, Weikai Yang
Published at Vis, 2025

Although data visualization is powerful for revealing patterns and communicating insights, creating effective visualizations requires familiarity with authoring tools and often disrupts the analysis flow. While large language models show promise for automatically converting analysis intent into visualizations, existing methods function as black boxes without transparent reasoning processes, which prevents users from understanding design rationales and refining suboptimal outputs. To bridge this gap, we propose integrating Chain-of-Thought (CoT) reasoning into the Natural Language to Visualization (NL2VIS) pipeline. First, we design a comprehensive CoT reasoning process for NL2VIS and develop an automatic pipeline to equip existing datasets with structured reasoning steps. Second, we introduce nvBench-CoT, a specialized dataset capturing detailed step-by-step reasoning from ambiguous natural language descriptions to finalized visualizations, which enables state-of-the-art performance when used for model fine-tuning. Third, we develop DeepVIS, an interactive visual interface that tightly integrates with the CoT reasoning process, allowing users to inspect reasoning steps, identify errors, and make targeted adjustments to improve visualization outcomes. Quantitative benchmark evaluations, two use cases, and a user study collectively demonstrate that our CoT framework effectively enhances NL2VIS quality while providing insightful reasoning steps to users.
Design Space and Declarative Grammar for 3D Genomic Data Visualization
Authors: David Kouril, Trevor Manz, Sehi L'Yi, Nils Gehlenborg
Published at Vis, 2025

Various computational approaches predict chromatin structure, yielding concrete models that position genomic loci in physical space and help reveal genome organization and function. While prior visualization research has explored data and task abstractions for genomics, the design space for depicting these three-dimensional (3D) genome models—and associated genome-mapped data—remains unclear. In this paper, we investigate the visualization of genomic data with a spatial component. First, we systematically survey how 3D genome models are used and depicted in computational biology. We analyze over 300 papers with figures that visualize 3D genomic data and categorize the methods for visual representation. From this survey, we derive a design space for visualizing 3D genome data, identifying common patterns and key properties such as representation, visual channels, and composition. We position these findings within an existing genomics visualization taxonomy, refining and extending existing classifications. Second, we augment Gosling, a declarative visualization grammar for genomics, to support 3D genomic data. Our integration enables expressive authoring of visualizations that connect traditional genome-mapped information with 3D genome models, emphasizing their spatial characteristics. To demonstrate its utility, we employ our extended grammar to recreate interactive examples, showcasing its ability to represent complex visual designs. Comprehensive examples and an interactive editor are available at 3d.gosling-lang.org.
Designing for Disclosure in Data Visualizations
Authors: Krisha Mehta, Gordon Kindlmann, Alex Kale
Published at Vis, 2025

Visualizing data often entails data transformations that can reveal and hide information, operations we dub disclosure tactics. Whether designers hide information intentionally or as an implicit consequence of other design choices, tools and frameworks for visualization offer little explicit guidance on disclosure. To systematically characterize how visualizations can limit access to an underlying dataset, we contribute a content analysis of 425 examples of visualization techniques sampled from academic papers in the visualization literature, resulting in a taxonomy of disclosure tactics. Our taxonomy organizes disclosure tactics based on how they change the data representation underlying a chart, providing a systematic way to reason about design trade-offs in terms of what information is revealed, distorted, or hidden. We demonstrate the benefits of using our taxonomy by showing how it can guide reasoning in design scenarios where disclosure is a first-order consideration. Adopting disclosure as a framework for visualization research offers new perspective on authoring tools, literacy, uncertainty communication, personalization, and ethical design.