Category: Publications

A Comparative Study Of The Perceptual Sensitivity Of Topological Visualizations To Feature Variations

This paper evaluates the sensitivity of topology-based isocontour, Reeb graph, and persistence diagram visualizations compared to a reference color map visualization for synthetically generated scalar fields on 2-manifold triangular meshes embedded in 3D. In particular, we built and ran a human-subject study that evaluated the perception of data features characterized by Gaussian signals and measured how effectively each visualization technique portrays variations of data features arising from the position and amplitude variation of a mixture of Gaussians.

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CLAMS: Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering

We introduce CLAMS, a data-driven visual quality measure for automatically predicting cluster ambiguity in monochrome scatterplots. We first conduct a qualitative study to identify key factors that affect the visual separation of clusters (e.g., proximity or size difference between clusters). Based on the study findings, we deploy a regression module that estimates the human-judged separability of two clusters. Then, CLAMS predicts cluster ambiguity by analyzing the aggregated results of all pairwise separability between clusters that are generated by the module. CLAMS outperforms widely-used clustering techniques in predicting ground truth cluster ambiguity.

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Topological Deep Learning: Going Beyond Graph Data

In this paper, we present a unifying deep learning framework built upon a richer data structure that includes widely adopted topological domains. Specifically, we first introduce combinatorial complexes, a novel type of topological domain. Second, we develop a general class of message-passing combinatorial complex neural networks (CCNNs), focusing primarily on attention-based CCNNs. Third, we evaluate the performance of CCNNs on tasks related to mesh shape analysis and graph learning.

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Exploring Annotation Strategies In Professional Visualizations: Insights From Prominent US News Portals

Annotations play a vital role in visualizations, providing valuable insights and focusing attention on critical visual elements. This study analyzes a curated corpus of 72 professionally designed static charts with annotations from prominent US news portals including The New York Times, The Economists, The Wall Street Journal, and The Washington Post. The analysis reveals common patterns in annotation strategies used by professionals.

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Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper On Graphs

We apply the mapper construction—a popular tool in topological data analysis—to graph visualization, which provides a strong theoretical basis for summarizing the data while preserving their core structures. We develop a variation of the mapper construction targeting weighted, undirected graphs, called Mapper on Graphs, which generates homology-preserving skeletons of graphs. We further show how the adjustment of a single parameter enables multi-scale skeletonization of the input graph. Finally, we provide a software tool that enables interactive explorations of such skeletons and demonstrates the effectiveness of our method for synthetic and real-world data.

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Untangling Force-Directed Layouts Using Persistent Homology

In this paper, we use the principles of persistent homology to untangle force-directed layouts thus mitigating these issues. First, we devise a new method to use 0-dimensional persistent homology to efficiently generate an initial graph layout, resulting in faster convergence and better quality graph layouts. Second, we provide an efficient algorithm for 1-dimensional persistent homology features and provide users the ability to interact with the 1-dimensional features by highlighting them and adding cycle-emphasizing forces to the layout.

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A Qualitative Evaluation And Taxonomy Of Student Annotations On Bar Charts

Annotations have become an essential part of visualizations, primarily when externalizing data or engaging in collaborative analysis. Therefore, it is crucial to understand how people annotate visualizations. This two-phase study used individual and group settings to investigate how visualization students annotate bar charts when asked to answer high-level questions about the data in the charts. The resulting annotations were coded and summarized into a taxonomy with several interesting findings.

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Automatic Scatterplot Design Optimization For Clustering Identification

In this paper, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image.

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AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing

We present an approach utilizing Topological Data Analysis to study the structure of face poses used in affective computing, i.e., the process of recognizing human emotion. The approach uses a conditional comparison of different emotions, both respective and irrespective of time, with multiple topological distance metrics, dimension reduction techniques, and face subsections (e.g., eyes, nose, mouth, etc.). Continue reading

CleanAirNowKC: Building Community Power by Improving Data Accessibility

In this paper, we have implemented an interactive map that can help CleanAirNowKC community members to monitor air quality efficiently. The system also allows for reporting and tracking industrial emissions or toxic releases, which will further help identify major contributors to pollution. These resources can serve an important role as evidence that will assist in advocating for community-driven just policies to improve the air quality regulation in Kansas City. Continue reading