1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
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…
cs.HC2024
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…
cs.LG2022★ 1 cited
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…