Category: Publications

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.

Continue reading

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.

Continue reading

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.

Continue reading

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.

Continue reading

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.

Continue reading

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

Through The Looking Glass: Insights Into Visualization Pedagogy Through Sentiment Analysis Of Peer Review Text

We discuss the construction and application of peer review in two visualization courses from different colleges at the University of South Florida. We then analyze student projects and peer review text via sentiment analysis to infer insights for visualization educators, including the focus of course content, engagement across student groups, student mastery of concepts, course trends over time, and expert intervention effectiveness.

Continue reading

Polarity In The Classroom: An Application Leveraging Peer Sentiment Towards Scalable Assessment

In this work, we detail the process by which we create our domain-dependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm which provides a fine-grained sentiment score from text alone. We analyze the validity from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses.

Continue reading