10 papers
Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
Daniel Hangan, Stephen Kobourov, Jacob Miller
Both Dimensionality Reduction (DR) and Graph Drawing (GD) aim to visualize abstract, non-linear structures, yet rely on different optimization paradigms. This contrast is evident i…
Class Angular Distortion Index for Dimensionality Reduction
Kaviru Gunaratne, Stephen Kobourov, Jacob Miller
Dimensionality reduction (DR) techniques are often characterized by whether they preserve global, high-level structures in the data or local, neighborhood structures. This distinct…
ReTrace: Interactive Visualizations for Reasoning Traces of Large Reasoning Models
Ludwig Felder, Jacob Miller, Markus Wallinger +2
Recent advances in Large Language Models have led to Large Reasoning Models, which produce step-by-step reasoning traces. These traces offer insight into how models think and their…
Visualization Biases MLLM's Decision Making in Network Data Tasks
Timo Brand, Henry Förster, Stephen G. Kobourov +1
We evaluate how visualizations can influence the judgment of MLLMs about the presence or absence of bridges in a network. We show that the inclusion of visualization improves confi…
How Scale Breaks "Normalized Stress" and KL Divergence: Rethinking Quality Metrics
Kiran Smelser, Kaviru Gunaratne, Jacob Miller +1
Complex, high-dimensional data is ubiquitous across many scientific disciplines, including machine learning, biology, and the social sciences. One of the primary methods of visuali…
Drawing Trees and Cacti with Integer Edge Lengths on a Polynomial-Size Grid
Henry Förster, Stephen Kobourov, Jacob Miller +1
A strengthened version of Harborth's well-known conjecture -- known as Kleber's conjecture -- states that every planar graph admits a planar straight-line drawing where every edge…