DevLan/vispubs huggingface dataset:
Does This Have a Particular Meaning? Interactive Pattern Explanation for Network Visualizations
Authors: Xinhuan Shu, Alexis Pister, Junxiu Tang, Fanny Chevalier, Benjamin Bach
Published at Vis, 2024

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer's selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology
DracoGPT: Extracting Visualization Design Preferences from Large Language Models
Authors: Huichen Will Wang, Mitchell Gordon, Leilani Battle, Jeffrey Heer
Published at Vis, 2024

Trained on vast corpora, Large Language Models (LLMs) have the potential to encode visualization design knowledge and best practices. However, if they fail to do so, they might provide unreliable visualization recommendations. What visualization design preferences, then, have LLMs learned? We contribute DracoGPT, a method for extracting, modeling, and assessing visualization design preferences from LLMs. To assess varied tasks, we develop two pipelines—DracoGPT-Rank and DracoGPT-Recommend—to model LLMs prompted to either rank or recommend visual encoding specifications. We use Draco as a shared knowledge base in which to represent LLM design preferences and compare them to best practices from empirical research. We demonstrate that DracoGPT can accurately model the preferences expressed by LLMs, enabling analysis in terms of Draco design constraints. Across a suite of backing LLMs, we find that DracoGPT-Rank and DracoGPT-Recommend moderately agree with each other, but both substantially diverge from guidelines drawn from human subjects experiments. Future work can build on our approach to expand Draco's knowledge base to model a richer set of preferences and to provide a robust and cost-effective stand-in for LLMs.
Dynamic Color Assignment for Hierarchical Data
Authors: Jiashu Chen, Weikai Yang, Zelin Jia, Lanxi Xiao, Shixia Liu
Published at Vis, 2024

Assigning discriminable and harmonic colors to samples according to their class labels and spatial distribution can generate attractive visualizations and facilitate data exploration. However, as the number of classes increases, it is challenging to generate a high-quality color assignment result that accommodates all classes simultaneously. A practical solution is to organize classes into a hierarchy and then dynamically assign colors during exploration. However, existing color assignment methods fall short in generating high-quality color assignment results and dynamically aligning them with hierarchical structures. To address this issue, we develop a dynamic color assignment method for hierarchical data, which is formulated as a multi-objective optimization problem. This method simultaneously considers color discriminability, color harmony, and spatial distribution at each hierarchical level. By using the colors of parent classes to guide the color assignment of their child classes, our method further promotes both consistency and clarity across hierarchical levels. We demonstrate the effectiveness of our method in generating dynamic color assignment results with quantitative experiments and a user study.
Entanglements for Visualization: Changing Research Outcomes through Feminist Theory
Authors: Derya Akbaba, Lauren F. Klein, Miriah Meyer
Published at Vis, 2024

A growing body of work draws on feminist thinking to challenge assumptions about how people engage with and use visualizations. This work draws on feminist values, driving design and research guidelines that account for the influences of power and neglect. This prior work is largely prescriptive, however, forgoing articulation of how feminist theories of knowledge — or feminist epistemology — can alter research design and outcomes. At the core of our work is an engagement with feminist epistemology, drawing attention to how a new framework for how we know what we know enabled us to overcome intellectual tensions in our research. Specifically, we focus on the theoretical concept of entanglement, central to recent feminist scholarship, and contribute: a history of entanglement in the broader scope of feminist theory; an articulation of the main points of entanglement theory for a visualization context; and a case study of research outcomes as evidence of the potential of feminist epistemology to impact visualization research. This work answers a call in the community to embrace a broader set of theoretical and epistemic foundations and provides a starting point for bringing feminist theories into visualization research.
Evaluating and extending speedup techniques for optimal crossing minimization in layered graph drawings
Authors: Connor Wilson, Eduardo Puerta, Tarik Crnovrsanin, Sara Di Bartolomeo, Cody Dunne
Published at Vis, 2024

A layered graph is an important category of graph in which every node is assigned to a layer, and layers are drawn as parallel or radial lines. They are commonly used to display temporal data or hierarchical graphs. Previous research has demonstrated that minimizing edge crossings is the most important criterion to consider when looking to improve the readability of such graphs. While heuristic approaches exist for crossing minimization, we are interested in optimal approaches to the problem that prioritize human readability over computational scalability. We aim to improve the usefulness and applicability of such optimal methods by understanding and improving their scalability to larger graphs. This paper categorizes and evaluates the state-of-the-art linear programming formulations for exact crossing minimization and describes nine new and existing techniques that could plausibly accelerate the optimization algorithm. Through a computational evaluation, we explore each technique's effect on calculation time and how the techniques assist or inhibit one another, allowing researchers and practitioners to adapt them to the characteristics of their graphs. Our best-performing techniques yielded a median improvement of 2.5–17× depending on the solver used, giving us the capability to create optimal layouts faster and for larger graphs. We provide an open-source implementation of our methodology in Python, where users can pick which combination of techniques to enable according to their use case. A free copy of this paper and all supplemental materials, datasets used, and source code are available at https://osf.io/5vq79.
Evaluating Force-based Haptics for Immersive Tangible Interactions with Surface Visualizations
Authors: Hamza Afzaal, Usman R. Alim
Published at Vis, 2024

Haptic feedback provides an essential sensory stimulus crucial for interaction and analyzing three-dimensional spatio-temporal phenomena on surface visualizations. Given its ability to provide enhanced spatial perception and scene maneuverability, virtual reality (VR) catalyzes haptic interactions on surface visualizations. Various interaction modes, encompassing both mid-air and on-surface interactions—with or without the application of assisting force stimuli—have been explored using haptic force feedback devices. In this paper, we evaluate the use of on-surface and assisted on-surface haptic modes of interaction compared to a no-haptic interaction mode. A force-based haptic stylus is used for all three modalities; the on-surface mode uses collision based forces, whereas the assisted on-surface mode is accompanied by an additional snapping force. We conducted a within-subjects user study involving fundamental interaction tasks performed on surface visualizations. Keeping a consistent visual design across all three modes, our study incorporates tasks that require the localization of the highest, lowest, and random points on surfaces; and tasks that focus on brushing curves on surfaces with varying complexity and occlusion levels. Our findings show that participants took almost the same time to brush curves using all the interaction modes. They could draw smoother curves using the on-surface interaction modes compared to the no-haptic mode. However, the assisted on-surface mode provided better accuracy than the on-surface mode. The on-surface mode was slower in point localization, but the accuracy depended on the visual cues and occlusions associated with the tasks. Finally, we discuss participant feedback on using haptic force feedback as a tangible input modality and share takeaways to aid the design of haptics-based tangible interactions for surface visualizations.
Fast Comparative Analysis of Merge Trees Using Locality Sensitive Hashing
Authors: Weiran Lyu, Raghavendra Sridharamurthy, Jeff M. Phillips, Bei Wang 0001
Published at Vis, 2024

Scalar field comparison is a fundamental task in scientific visualization. In topological data analysis, we compare topological descriptors of scalar fields—such as persistence diagrams and merge trees—because they provide succinct and robust abstract representations. Several similarity measures for topological descriptors seem to be both asymptotically and practically efficient with polynomial time algorithms, but they do not scale well when handling large-scale, time-varying scientific data and ensembles. In this paper, we propose a new framework to facilitate the comparative analysis of merge trees, inspired by tools from locality sensitive hashing (LSH). LSH hashes similar objects into the same hash buckets with high probability. We propose two new similarity measures for merge trees that can be computed via LSH, using new extensions to Recursive MinHash and subpath signature, respectively. Our similarity measures are extremely efficient to compute and closely resemble the results of existing measures such as merge tree edit distance or geometric interleaving distance. Our experiments demonstrate the utility of our LSH framework in applications such as shape matching, clustering, key event detection, and ensemble summarization.
Ferry: Toward Better Understanding of Input/Output Space for Data Wrangling Scripts
Authors: Zhongsu Luo, Kai Xiong, Jiajun Zhu, Ran Chen, Xinhuan Shu, Di Weng, Yingcai Wu
Published at Vis, 2024

Understanding the input and output of data wrangling scripts is crucial for various tasks like debugging code and onboarding new data. However, existing research on script understanding primarily focuses on revealing the process of data transformations, lacking the ability to analyze the potential scope, i.e., the space of script inputs and outputs. Meanwhile, constructing input/output space during script analysis is challenging, as the wrangling scripts could be semantically complex and diverse, and the association between different data objects is intricate. To facilitate data workers in understanding the input and output space of wrangling scripts, we summarize ten types of constraints to express table space and build a mapping between data transformations and these constraints to guide the construction of the input/output for individual transformations. Then, we propose a constraint generation model for integrating table constraints across multiple transformations. Based on the model, we develop Ferry, an interactive system that extracts and visualizes the data constraints describing the input and output space of data wrangling scripts, thereby enabling users to grasp the high-level semantics of complex scripts and locate the origins of faulty data transformations. Besides, Ferry provides example input and output data to assist users in interpreting the extracted constraints and checking and resolving the conflicts between these constraints and any uploaded dataset. Ferry's effectiveness and usability are evaluated through two usage scenarios and two case studies, including understanding, debugging, and checking both single and multiple scripts, with and without executable data. Furthermore, an illustrative application is presented to demonstrate Ferry's flexibility.
Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education
Authors: Lin Gao, Jing Lu, Zekai Shao 0001, Ziyue Lin, Shengbin Yue, Chiokit Leong, Yi Sun, Rory James Zauner, Zhongyu Wei, Siming Chen 0001
Published at Vis, 2024

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domainspecific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.
FPCS: Feature Preserving Compensated Sampling of Streaming Time Series Data
Authors: Hongyan Li, Bo Yang, Yansong Chua
Published at Vis, 2024

Data visualization aids in making data analysis more intuitive and in-depth, with widespread applications in fields such as biology, finance, and medicine. For massive and continuously growing streaming time series data, these data are typically visualized in the form of line charts, but the data transmission puts significant pressure on the network, leading to visualization lag or even failure to render completely. This paper proposes a universal sampling algorithm FPCS, which retains feature points from continuously received streaming time series data, compensates for the frequent fluctuating feature points, and aims to achieve efficient visualization. This algorithm bridges the gap in sampling for streaming time series data. The algorithm has several advantages: (1) It optimizes the sampling results by compensating for fewer feature points, retaining the visualization features of the original data very well, ensuring high-quality sampled data; (2) The execution time is the shortest compared to similar existing algorithms; (3) It has an almost negligible space overhead; (4) The data sampling process does not depend on the overall data; (5) This algorithm can be applied to infinite streaming data and finite static data
From Instruction to Insight: Exploring the Functional and Semantic Roles of Text in Interactive Dashboards
Authors: Nicole Sultanum, Vidya Setlur
Published at Vis, 2024

There is increased interest in understanding the interplay between text and visuals in the field of data visualization. However, this attention has predominantly been on the use of text in standalone visualizations (such as text annotation overlays) or augmenting text stories supported by a series of independent views. In this paper, we shift from the traditional focus on single-chart annotations to characterize the nuanced but crucial communication role of text in the complex environment of interactive dashboards. Through a survey and analysis of 190 dashboards in the wild, plus 13 expert interview sessions with experienced dashboard authors, we highlight the distinctive nature of text as an integral component of the dashboard experience, while delving into the categories, semantic levels, and functional roles of text, and exploring how these text elements are coalesced by dashboard authors to guide and inform dashboard users. Our contributions are threefold. First, we distill qualitative and quantitative findings from our studies to characterize current practices of text use in dashboards, including a categorization of text-based components and design patterns. Second, we leverage current practices and existing literature to propose, discuss, and validate recommended practices for text in dashboards, embodied as a set of 12 heuristics that underscore the semantic and functional role of text in offering navigational cues, contextualizing data insights, supporting reading order, among other concerns. Third, we reflect on our findings to identify gaps and propose opportunities for data visualization researchers to push the boundaries on text usage for dashboards, from authoring support and interactivity to text generation and content personalization. Our research underscores the significance of elevating text as a first-class citizen in data visualization, and the need to support the inclusion of textual components and their interactive affordances in dashboard design.
Graph Transformer for Label Placement
Authors: Jingwei Qu, Pingshun Zhang, Enyu Che, Yinan Chen, Haibin Ling
Published at Vis, 2024

Placing text labels is a common way to explain key elements in a given scene. Given a graphic input and original label information, how to place labels to meet both geometric and aesthetic requirements is an open challenging problem. Geometry-wise, traditional rule-driven solutions struggle to capture the complex interactions between labels, let alone consider graphical/appearance content. In terms of aesthetics, training/evaluation data ideally require nontrivial effort and expertise in design, thus resulting in a lack of decent datasets for learning-based methods. To address the above challenges, we formulate the task with a graph representation, where nodes correspond to labels and edges to interactions between labels, and treat label placement as a node position prediction problem. With this novel representation, we design a Label Placement Graph Transformer (LPGT) to predict label positions. Specifically, edge-level attention, conditioned on node representations, is introduced to reveal potential relationships between labels. To integrate graphic/image information, we design a feature aligning strategy that extracts deep features for nodes and edges efficiently. Next, to address the dataset issue, we collect commercial illustrations with professionally designed label layouts from household appliance manuals, and annotate them with useful information to create a novel dataset named the Appliance Manual Illustration Labels (AMIL) dataset. In the thorough evaluation on AMIL, our LPGT solution achieves promising label placement performance compared with popular baselines. Our algorithm and dataset are available at https://github.com/JingweiQu/LPGT.
HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data
Authors: Guozheng Li 0002, Haotian Mi, Chi Harold Liu, Takayuki Itoh, Guoren Wang
Published at Vis, 2024

When using exploratory visual analysis to examine multivariate hierarchical data, users often need to query data to narrow down the scope of analysis. However, formulating effective query expressions remains a challenge for multivariate hierarchical data, particularly when datasets become very large. To address this issue, we develop a declarative grammar, HiRegEx (Hierarchical data Regular Expression), for querying and exploring multivariate hierarchical data. Rooted in the extended multi-level task topology framework for tree visualizations (e-MLTT), HiRegEx delineates three query targets (node, path, and subtree) and two aspects for querying these targets (features and positions), and uses operators developed based on classical regular expressions for query construction. Based on the HiRegEx grammar, we develop an exploratory framework for querying and exploring multivariate hierarchical data and integrate it into the TreeQueryER prototype system. The exploratory framework includes three major components: top-down pattern specification, bottom-up data-driven inquiry, and context-creation data overview. We validate the expressiveness of HiRegEx with the tasks from the e-MLTT framework and showcase the utility and effectiveness of TreeQueryER system through a case study involving expert users in the analysis of a citation tree dataset
How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts
Authors: Huichen Will Wang, Jane Hoffswell, Sao Myat Thazin Thane, Victor S. Bursztyn, Cindy Xiong Bearfield
Published at Vis, 2024

Large Language Models (LLMs) have been adopted for a variety of visualizations tasks, but how far are we from perceptually aware LLMs that can predict human takeaways? Graphical perception literature has shown that human chart takeaways are sensitive to visualization design choices, such as spatial layouts. In this work, we examine the extent to which LLMs exhibit such sensitivity when generating takeaways, using bar charts with varying spatial layouts as a case study. We conducted three experiments and tested four common bar chart layouts: vertically juxtaposed, horizontally juxtaposed, overlaid, and stacked. In Experiment 1, we identified the optimal configurations to generate meaningful chart takeaways by testing four LLMs, two temperature settings, nine chart specifications, and two prompting strategies. We found that even state-of-the-art LLMs struggled to generate semantically diverse and factually accurate takeaways. In Experiment 2, we used the optimal configurations to generate 30 chart takeaways each for eight visualizations across four layouts and two datasets in both zero-shot and one-shot settings. Compared to human takeaways, we found that the takeaways LLMs generated often did not match the types of comparisons made by humans. In Experiment 3, we examined the effect of chart context and data on LLM takeaways. We found that LLMs, unlike humans, exhibited variation in takeaway comparison types for different bar charts using the same bar layout. Overall, our case study evaluates the ability of LLMs to emulate human interpretations of data and points to challenges and opportunities in using LLMs to predict human chart takeaways.