181 citations · 310 across the 26 of their papers we have counts for
27 papers
ggaction: A Grammar of Graphical Actions
Hyeon Jeon, Jinwook Seo
A chart may be declarative; authoring it is not. Visualization grammars often describe charts as finished specifications, whereas people construct them through a sequence of author…
TailVis: Expressive Chart Refinement Preserving Data-Binding Integrity
Yumin Song, Seokhyeon Park, Soohyun Lee +4
Creating static visualizations for presentations and publications requires granular refinements of visual details, even for simple charts. Existing data-driven visualization tools…
HookLens: Visual Analytics for Understanding React Hooks Structures
Suyeon Hwang, Minkyu Kweon, Jeongmin Rhee +5
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…
Seeing Graphs Like Humans: Benchmarking Computational Measures and MLLMs for Similarity Assessment
Seokweon Jung, Jeongmin Rhee, Seoyoung Doh +3
Comparing graphs to identify similarities is a fundamental task in visual analytics of graph data. To support this, visual analytics systems frequently employ quantitative computat…
InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation
Junhyeong Hwangbo, Soohyun Lee, Hyeon Jeon +4
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-proba…
Understanding Bias in Perceiving Dimensionality Reduction Projections
Seoyoung Doh, Hyeon Jeon, Sungbok Shin +3
Selecting the dimensionality reduction technique that faithfully represents the structure is essential for reliable visual communication and analytics. In reality, however, practit…
Dataset-Adaptive Dimensionality Reduction
Hyeon Jeon, Jeongin Park, Soohyun Lee +3
Selecting the appropriate dimensionality reduction (DR) technique and determining its optimal hyperparameter settings that maximize the accuracy of the output projections typically…
Metric Design != Metric Behavior: Improving Metric Selection for the Unbiased Evaluation of Dimensionality Reduction
Jiyeon Bae, Hyeon Jeon, Jinwook Seo
Evaluating the accuracy of dimensionality reduction (DR) projections in preserving the structure of high-dimensional data is crucial for reliable visual analytics. Diverse evaluati…
Can VLMs Assess Similarity Between Graph Visualizations?
Seokweon Jung, Hyeon Jeon, Jeongmin Rhee +1
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…
Measuring the Validity of Clustering Validation Datasets
Hyeon Jeon, Michaël Aupetit, DongHwa Shin +3
Clustering techniques are often validated using benchmark datasets where class labels are used as ground-truth clusters. However, depending on the datasets, class labels may not al…
Efficiently Crowdsourcing Visual Importance with Punch-Hole Annotation
Minsuk Chang, Soohyun Lee, Aeri Cho +4
We introduce a novel crowdsourcing method for identifying important areas in graphical images through punch-hole labeling. Traditional methods, such as gaze trackers and mouse-base…
PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets
Jaeyoung Kim, Sihyeon Lee, Hyeon Jeon +4
Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke,…
Offsetting Perceptual Bias in Visual Clustering: The Role of Point Size Adjustment in Variable Display Sizes
Taehyun Yang, Hyeon Jeon, Jinwook Seo
Scatterplots are frequently shared across different displays in collaborative and communicative visual analytics. However, variations in displays diversify scatterplot sizes. Such…
Combinational Nonuniform Timeslicing of Dynamic Networks
Seokweon Jung, DongHwa Shin, Hyeon Jeon +1
Dynamic networks represent the complex and evolving interrelationships between real-world entities. Given the scale and variability of these networks, finding an optimal slicing in…
Extracting Human Attention through Crowdsourced Patch Labeling
Minsuk Chang, Seokhyeon Park, Hyeon Jeon +3
In image classification, a significant problem arises from bias in the datasets. When it contains only specific types of images, the classifier begins to rely on shortcuts - simpli…
CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
Juhye Ha, Hyeon Jeon, DaEun Han +2
Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. How…
Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models
Hyung-Kwon Ko, Hyeon Jeon, Gwanmo Park +4
We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using only Vega-Lite specifications as input, thereby streamlining the develo…
CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering
Hyeon Jeon, Ghulam Jilani Quadri, Hyunwook Lee +3
Visual clustering is a common perceptual task in scatterplots that supports diverse analytics tasks (e.g., cluster identification). However, even with the same scatterplot, the way…
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings
Hyeon Jeon, Aeri Cho, Jinhwa Jang +5
Dimensionality reduction (DR) techniques inherently distort the original structure of input high-dimensional data, producing imperfect low-dimensional embeddings. Diverse distortio…
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction
Hyeon Jeon, Yun-Hsin Kuo, Michaël Aupetit +2
A common way to evaluate the reliability of dimensionality reduction (DR) embeddings is to quantify how well labeled classes form compact, mutually separated clusters in the embedd…
Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works
Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon +3
Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating hi…
Sanity Check for External Clustering Validation Benchmarks using Internal Validation Measures
Hyeon Jeon, Michael Aupetit, DongHwa Shin +3
We address the lack of reliability in benchmarking clustering techniques based on labeled datasets. A standard scheme in external clustering validation is to use class labels as gr…
Uniform Manifold Approximation with Two-phase Optimization
Hyeon Jeon, Hyung-Kwon Ko, Soohyun Lee +2
We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of hig…
Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections
Hyeon Jeon, Michaël Aupetit, Soohyun Lee +4
Brushing is a common interaction technique in 2D scatterplots, allowing users to select clustered points within a continuous, enclosed region for further analysis or filtering. How…