1 citations · 1 across the 16 of their papers we have counts for
20 papers
Minimum-Width Drawing of Trees with Sized Vertices
Markus Wallinger, Oscar Navarro, Stephen G. Kobourov
Trees arise in many applications and computing nice tree layouts is a classical problem in information visualization. In many practical settings, vertices need to be represented as…
3DMPE: 3D Multi-Perspective Embedding
Vahan Huroyan, Md Rahat-uz-Zaman, Stephen Kobourov
We study 3D point cloud reconstruction from multiple partially observed 2D projections. Given two or more projections of an unknown 3D point cloud, together with cross-view point c…
Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections
Ya Ji, Xuefeng Li, Timo Brand +4
Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure. We propose to embed graphs…
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…
Embedding Trees and Cactus Graphs with Integer Edge Lengths on a Compact Integer Grid
Henry Förster, Aaron Büngener, Stephen Kobourov +2
Every planar graph can be embedded with crossing-free, straight-line edges on an integer grid, where is the number of vertices. It is not known, however, whe…
Using Reinforcement Learning to Optimize the Global and Local Crossing Number
Timo Brand, Henry Förster, Stephen Kobourov +4
Graph drawing concerns the algorithmic visualization of graphs. A good drawing of a graph is easy to read and facilitates solving tasks on the graph. Several properties have been i…
Exploring MLLMs Perception of Network Visualization Principles
Jacob Miller, Markus Wallinger, Ludwig Felder +5
In this paper, we test whether Multimodal Large Language Models (MLLMs) can match human-subject performance in tasks involving the perception of properties in network layouts. Spec…
StorySets: Ordering Curves and Dimensions for Visualizing Uncertain Sets and Multi-Dimensional Discrete Data
Markus Wallinger, Annika Bonerath, Wouter Meulemans +3
We propose a method for visualizing uncertain set systems, which differs from previous set visualization approaches that are based on certainty (an element either belongs to a set…
Representing Hypergraphs by Point-Line Incidences
Alexander Dobler, Stephen Kobourov, Debajyoti Mondal +1
We consider hypergraph visualizations that represent vertices as points in the plane and hyperedges as curves passing through the points of their incident vertices. Specifically, w…
The Perception of Stress in Graph Drawings
Gavin J. Mooney, Helen C. Purchase, Michael Wybrow +2
Most of the common graph layout principles (a.k.a. "aesthetics") on which many graph drawing algorithms are based are easy to define and to perceive. For example, the number of pai…
GraphTrials: Visual Proofs of Graph Properties
Henry Förster, Felix Klesen, Tim Dwyer +8
Graph and network visualization supports exploration, analysis and communication of relational data arising in many domains: from biological and social networks, to transportation…
"Normalized Stress" is Not Normalized: How to Interpret Stress Correctly
Kiran Smelser, Jacob Miller, Stephen Kobourov
Stress is among the most commonly employed quality metrics and optimization criteria for dimension reduction projections of high dimensional data. Complex, high dimensional data is…
Size Should Not Matter: Evaluating Network Visualizations with Stress
Reyan Ahmed, Cesim Erten, Stephen Kobourov +3
The normalized stress metric is widely used to assess graph drawing quality, measuring how closely distances between vertices in a layout match their graph-theoretic distances. Thi…
Wooly Graphs : A Mathematical Framework For Knitting
Kathryn Gray, Brian Bell, Diana Sieper +4
This paper aims to develop a mathematical foundation to model knitting with graphs. We provide a precise definition for knit objects with a knot theoretic component and propose a s…
A Graph Model and a Layout Algorithm for Knitting Patterns
Kathryn Gray, Brian Bell, Stephen Kobourov
Knitting, an ancient fiber art, creates a structured fabric consisting of loops or stitches. Publishing hand knitting patterns involves lengthy testing periods and numerous knitter…
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE
Jacob Miller, Vahan Huroyan, Raymundo Navarrete +2
When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an a…