arXiv subjects

Hyeon Jeon

Publications and source records attributed to Hyeon Jeon.

At least 19 recordsLinked to original sources

arXiv2026

TailVis: Expressive Chart Refinement Preserving Data-Binding Integrity

Creating static visualizations for presentations and publications requires granular refinements of visual details, even for simple charts. Existing data-driven visualization tools offer limited interactive control for such refinements, forcing users to export charts to external graphic editors and breaking the critical link between data and visual representation. To address this gap, we propose an extended InfoVis Reference Model to account for post-render design refinement. A formative study with 18 visualization practitioners and a follow-up survey of 35 respondents confirmed that this stage is pervasive yet unsupported in current practice. Based on these findings, we present TailVis, a visualization authoring system that enables expressive visual customization while preserving data-binding integrity. TailVis supports element-level direct selection and scope expansion, allowing users to define a data-aware scope ranging from a single mark to a data-driven category with a simple selection. For modifications beyond predefined controls, TailVis blends natural language input with dynamically generated GUI widgets, where deictic interaction lets users reference elements simply by clicking them, keeping even open-ended edits bound to the data. To support rigorous exploration and comparison of design alternatives, TailVis implements a provenance history that enables users to capture diverse design iterations while ensuring data-visual integrity. A user study with 12 participants verified that TailVis effectively supports expressive, granular refinement without sacrificing data binding, significantly reducing repetitive manual processes in an integrated environment.

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arXiv2026

Visualization Autocomplete: Visualization Authoring via Stepwise Design Recommendations

When domain experts create charts, the bottleneck is rarely the data, but knowing the optimal next step in chart design. The visualization design space is vast, and while domain experts can recognize a good design when they see it, it is often challenging to determine the exact path to get there. To address this, we present VISAUTOCOMPLETE, a system inspired by text autocompletion that reconceptualizes visualization design as a sequential process, recommending concrete next steps at each stage of the authoring process based on common practices. Users can intervene at any step, or delegate multiple steps to the system and select one from the design recommendations. To support responsive interaction, we distill the translation logic of a large language model (LLM) into a single function that receives the current chart state and recommended transition as input and returns the updated chart specification as output. We evaluate the system against a LLM vibecoding, Microsoft Excel, and TaskVis, an automated chart recommendation engine, on chart quality and approachability. Our results show that VisAutocomplete outperforms all baselines in the articulacy of complex chart authoring, while remaining on par with LLM in approachability.

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arXiv2026

Seeing Graphs Like Humans: Benchmarking Computational Measures and MLLMs for Similarity Assessment

Comparing graphs to identify similarities is a fundamental task in visual analytics of graph data. To support this, visual analytics systems frequently employ quantitative computational measures to provide automated guidance. However, it remains unclear how well these measures align with subjective human visual perception, thereby offering recommendations that conflict with analysts' intuitive judgments, potentially leading to confusion rather than reducing cognitive load. Multimodal Large Language Models (MLLMs), capable of visually interpreting graphs and explaining their reasoning in natural language, have emerged as a potential alternative to address this challenge. This paper bridges the gap between human and machine assessment of graph similarity through three interconnected experiments using a dataset of 1,881 node-link diagrams. Experiment 1 collects relative similarity judgments and rationales from 32 human participants, revealing consensus on graph similarity while prioritizing global shapes and edge densities over exact topological details. Experiment 2 benchmarks 16 computational measures against these human judgments, identifying Portrait divergence as the best-performing metric, though with only moderate alignment. Experiment 3 evaluates the potential of three state-of-the-art MLLMs (GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.5) as perceptual proxies. The results demonstrate that MLLMs, particularly GPT-5, significantly outperform traditional measures in aligning with human graph similarity perception and provide interpretable rationales for their decisions, whereas Claude Sonnet 4.5 shows the best computational efficiency. Our findings suggest that MLLMs hold significant promise not only as effective, explainable proxies for human perception but also as intelligent guides that can uncover subtle nuances that might be overlooked by human analysts in visual analytics systems.

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arXiv2026

HookLens: Visual Analytics for Understanding React Hooks Structures

Maintaining and refactoring React web applications is challenging, as React code often becomes complex due to its core API called Hooks. For example, Hooks often lead developers to create complex dependencies among components, making code behavior unpredictable and reducing maintainability, i.e., anti-patterns. To address this challenge, we present HookLens, an interactive visual analytics system that helps developers understand howHooks define dependencies and data flows between components. Informed by an iterative design process with experienced React developers, HookLens supports users to efficiently understand the structure and dependencies between components and to identify anti-patterns. A quantitative user study with 12 React developers demonstrates that HookLens significantly improves participants' accuracy in detecting anti-patterns compared to conventional code editors. Moreover, a comparative study with state-of-the-art LLM-based coding assistants confirms that these improvements even surpass the capabilities of such coding assistants on the same task.

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arXiv2025

Dimensionality Reduction Considered Harmful (Some of the Time)

Visual analytics now plays a central role in decision-making across diverse disciplines, but it can be unreliable: the knowledge or insights derived from the analysis may not accurately reflect the underlying data. In this dissertation, we improve the reliability of visual analytics with a focus on dimensionality reduction (DR). DR techniques enable visual analysis of high-dimensional data by reducing it to two or three dimensions, but they inherently introduce errors that can compromise the reliability of visual analytics. To this end, I investigate reliability challenges that practitioners face when using DR for visual analytics. Then, I propose technical solutions to address these challenges, including new evaluation metrics, optimization strategies, and interaction techniques. We conclude the thesis by discussing how our contributions lay the foundation for achieving more reliable visual analytics practices.

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arXiv2025

InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation

Even LLMs that appear safe during evaluation can still produce harmful responses in deployment. Because stochastic sampling yields different responses to the same prompt, low-probability harmful outputs can still reach users at scale. Common human evaluation workflows generate many random samples per prompt and review them in static spreadsheets. The practice scales poorly, forcing evaluators to repeatedly reread near-duplicate prefixes. To address this, we present InFerActive, an interactive system that visualizes sampling results as a navigable tree of readable phrases, allowing evaluators to filter, explore, and extend the generation space on demand. InFerActive utilizes breadth-first sampling, a novel tree construction procedure that matches the harmful-response coverage of random sampling while requiring up to 5.0x fewer samples. Two controlled user studies (N = 12 each) demonstrate that InFerActive significantly improves evaluation efficiency and coverage over both spreadsheet and basic tree baselines.

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arXiv2025

Changing the Paradigm from Dynamic Queries to LLM-generated SQL Queries with Human Intervention

We propose leveraging Large Language Models (LLMs) as an interaction layer for medical visualization systems. In domains like healthcare, where users must navigate high-dimensional, coded, and heterogeneous datasets, LLM-generated queries enable expert medical users to express complex analytical intents in natural language. These intents are then translated into editable and executable queries, replacing the dynamic query interfaces used by traditional visualization systems built around sliders, check boxes, and drop-downs. This interaction model reduces visual clutter and eliminates the need for users to memorize field names or system codes, supporting fluid exploration, with the drawback of not exposing all the filtering criteria. We also reintroduce dynamic queries on demand to better support interactive exploration. We posit that medical users are trained to know the possible filtering options but challenged to remember the details of the attribute names and code values. We demonstrate this paradigm in ParcoursVis, our scalable EventFlow-inspired patient care pathway visualization system powered by the French National Health Data System, one of the largest health data repositories in the world.

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arXiv2025

UMATO: Bridging Local and Global Structures for Reliable Visual Analytics with Dimensionality Reduction

Due to the intrinsic complexity of high-dimensional (HD) data, dimensionality reduction (DR) techniques cannot preserve all the structural characteristics of the original data. Therefore, DR techniques focus on preserving either local neighborhood structures (local techniques) or global structures such as pairwise distances between points (global techniques). However, both approaches can mislead analysts to erroneous conclusions about the overall arrangement of manifolds in HD data. For example, local techniques may exaggerate the compactness of individual manifolds, while global techniques may fail to separate clusters that are well-separated in the original space. In this research, we provide a deeper insight into Uniform Manifold Approximation with Two-phase Optimization (UMATO), a DR technique that addresses this problem by effectively capturing local and global structures. UMATO achieves this by dividing the optimization process of UMAP into two phases. In the first phase, it constructs a skeletal layout using representative points, and in the second phase, it projects the remaining points while preserving the regional characteristics. Quantitative experiments validate that UMATO outperforms widely used DR techniques, including UMAP, in terms of global structure preservation, with a slight loss in local structure. We also confirm that UMATO outperforms baseline techniques in terms of scalability and stability against initialization and subsampling, making it more effective for reliable HD data analysis. Finally, we present a case study and a qualitative demonstration that highlight UMATO's effectiveness in generating faithful projections, enhancing the overall reliability of visual analytics using DR.

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arXiv2025

Understanding Bias in Perceiving Dimensionality Reduction Projections

Selecting the dimensionality reduction technique that faithfully represents the structure is essential for reliable visual communication and analytics. In reality, however, practitioners favor projections for other attractions, such as aesthetics and visual saliency, over the projection's structural faithfulness, a bias we define as visual interestingness. In this research, we conduct a user study that (1) verifies the existence of such bias and (2) explains why the bias exists. Our study suggests that visual interestingness biases practitioners' preferences when selecting projections for analysis, and this bias intensifies with color-encoded labels and shorter exposure time. Based on our findings, we discuss strategies to mitigate bias in perceiving and interpreting DR projections.

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arXiv2025

Dataset-Adaptive Dimensionality Reduction

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.

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arXiv2025

Metric Design != Metric Behavior: Improving Metric Selection for the Unbiased Evaluation of Dimensionality Reduction

Evaluating the accuracy of dimensionality reduction (DR) projections in preserving the structure of high-dimensional data is crucial for reliable visual analytics. Diverse evaluation metrics targeting different structural characteristics have thus been developed. However, evaluations of DR projections can become biased if highly correlated metrics--those measuring similar structural characteristics--are inadvertently selected, favoring DR techniques that emphasize those characteristics. To address this issue, we propose a novel workflow that reduces bias in the selection of evaluation metrics by clustering metrics based on their empirical correlations rather than on their intended design characteristics alone. Our workflow works by computing metric similarity using pairwise correlations, clustering metrics to minimize overlap, and selecting a representative metric from each cluster. Quantitative experiments demonstrate that our approach improves the stability of DR evaluation, which indicates that our workflow contributes to mitigating evaluation bias.

cs.LG↗

arXiv2025

Stop Misusing t-SNE and UMAP for Visual Analytics

Misuses of t-SNE and UMAP in visual analytics have become increasingly common. For example, although t-SNE and UMAP projections often do not faithfully reflect the original distances between clusters, practitioners frequently use them to investigate inter-cluster relationships. We investigate why this misuse occurs, and discuss methods to prevent it. To that end, we first review 136 papers to verify the prevalence of the misuse. We then interview researchers who have used dimensionality reduction (DR) to understand why such misuse occurs. Finally, we interview DR experts to examine why previous efforts failed to address the misuse. We find that the misuse of t-SNE and UMAP stems primarily from limited DR literacy among practitioners, and that existing attempts to address this issue -- mostly based on academic papers -- have been ineffective. Based on these insights, we discuss potential future research directions to mitigate the misuse.

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arXiv2025

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction

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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arXiv2025

Reading.help: Supporting EFL Readers with Proactive and On-Demand Explanation of English Grammar and Semantics

A large portion of texts is written in English, but readers who see English as a Foreign Language (EFL) often struggle to read texts accurately and swiftly. EFL readers seek help from professional teachers and mentors, which is limited and costly. In this paper, we explore how an intelligent reading tool can assist EFL readers. We conducted a case study with EFL readers in South Korea. We at first developed an LLM-based reading tool based on prior literature. We then revised the tool based on the feedback from a study with 15 South Korean EFL readers. The final tool, named Reading.help, helps EFL readers comprehend complex sentences and paragraphs with on-demand and proactive explanations. We finally evaluated the tool with 5 EFL readers and 2 EFL education professionals. Our findings suggest Reading.help could potentially help EFL readers self-learn English when they do not have access to external support.

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arXiv2025

Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models

Effective data visualization requires not only technical proficiency but also a deep understanding of the domain-specific context in which data exists. This context often includes tacit knowledge about data provenance, quality, and intended use, which is rarely explicit in the dataset itself. Motivated by growing demands to surface tacit knowledge, we present the Data Therapist, a web-based system that helps domain experts externalize such implicit knowledge through a mixed-initiative process combining iterative Q&A with interactive annotation. Powered by a large language model, the system automatically analyzes user-supplied datasets, prompts users with targeted questions, and supports annotation at varying levels of granularity. The resulting structured knowledge base can inform both human and automated visualization design. A qualitative study with expert pairs from Accounting, Political Science, and Computer Security revealed recurring patterns in how expert reason about their data and highlighted opportunities for AI support to enhance visualization design.

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arXiv2025

Beyond the Mirror: Personal Analytics through Visual Juxtaposition with Other People's Data

An individual's data can reveal facets of behavior and identity, but its interpretation is context dependent. We can easily identify various self-tracking applications that help people reflect on their lives. However, self-tracking confined to one person's data source may fall short in terms of objectiveness, and insights coming from various perspectives. To address this, we examine how those interpretations about a person's data can be augmented when the data are juxtaposed with that of others using anonymized online calendar logs from a schedule management app. We develop CALTREND, a visual analytics system that compares an individuals anonymized online schedule logs with using those from other people. Using CALTREND as a probe, we conduct a study with two domain experts, one in information technology and one in Korean herbal medicine. We report our observations on how comparative views help enrich the characterization of an individual based on the experts' comments. We find that juxtaposing personal data with others' can potentially lead to diverse interpretations of one dataset shaped by domain-specific mental models.

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arXiv2025

Can VLMs Assess Similarity Between Graph Visualizations?

Graph visualizations have been studied for tasks such as clustering and temporal analysis, but how these visual similarities relate to established graph similarity measures remains unclear. In this paper, we explore the potential of Vision Language Models (VLMs) to approximate human-like perception of graph similarity. We generate graph datasets of various sizes and densities and compare VLM-derived visual similarity scores with feature-based measures. Our findings indicate VLMs can assess graph similarity in a manner similar to feature-based measures, even though differences among the measures exist. In future work, we plan to extend our research by conducting experiments on human visual graph perception.

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arXiv2025

A Critical Analysis of the Usage of Dimensionality Reduction in Four Domains

Dimensionality reduction is used as an important tool for unraveling the complexities of high-dimensional datasets in many fields of science, such as cell biology, chemical informatics, and physics. Visualizations of the dimensionally reduced data enable scientists to delve into the intrinsic structures of their datasets and align them with established hypotheses. Visualization researchers have thus proposed many dimensionality reduction methods and interactive systems designed to uncover latent structures. At the same time, different scientific domains have formulated guidelines or common workflows for using dimensionality reduction techniques and visualizations for their respective fields. In this work, we present a critical analysis of the usage of dimensionality reduction in scientific domains outside of computer science. First, we conduct a bibliometric analysis of 21,249 academic publications that use dimensionality reduction to observe differences in the frequency of techniques across fields. Next, we conduct a survey of a 71-paper sample from four fields: biology, chemistry, physics, and business. Through this survey, we uncover common workflows, processes, and usage patterns, including the mixed use of confirmatory data analysis to validate a dataset and projection method and exploratory data analysis to then generate more hypotheses. We also find that misinterpretations and inappropriate usage is common, particularly in the visual interpretation of the resulting dimensionally reduced view. Lastly, we compare our observations with recent works in the visualization community in order to match work within our community to potential areas of impact outside our community.

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