Category: Publications

This study holistically explores visualization interpretation to examine the alignment between designers’ communicative goals and what their audience sees in a visualization, which we refer to as their textit{comprehension}. We found that statistics people effectively estimate from visualizations in classical graphical perception studies may differ from the patterns people intuitively comprehend in a visualization. We found that comprehension varies with a range of factors, including graph complexity and data distribution. Our study confirms the importance of defining visualization effectiveness from multiple perspectives to assess and inform visualization practices.

We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelX is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains.

This study looks at employing Naive Bayes modeling to analyze peer review data from an undergraduate visual literacy course over a five-year period (2017-2022). A key finding is the emphasis on the ‘lie factor’ in students’ comments when using the visual peer review rubric, highlighting areas for potential course content adaptation and instructional refinement. Our findings suggest that predictive modeling, as a tool to assess student comments, can provide novel insights into visual peer review. This could lead to impactful changes in course content, project modifications, and rubric enhancements, ultimately benefiting student learning and engagement in visual literacy courses.

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.

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.