2 citations · 2 across the 5 of their papers we have counts for
7 papers
Fast and Faithful Edge Bundling using Spectral Sparsification
Xingjue Jiang, Seok-Hee Hong, Amyra Meidiana +1
Edge bundling reduces the visual complexity of drawings of large and complex graphs by clustering "compatible" edges. However, it often introduces distortion by bundling "unrelated…
BH-tsNET, FIt-tsNET, L-tsNET: Fast tsNET Algorithms for Large Graph Drawing
Amyra Meidiana, Seok-Hee Hong, Kwan-Liu Ma
The tsNET algorithm utilizes t-SNE to compute high-quality graph drawings, preserving the neighborhood and clustering structure. We present three fast algorithms for reducing the t…
SS-GUMAP, SL-GUMAP, SSSL-GUMAP: Fast UMAP Algorithms for Large Graph Drawing
Amyra Meidiana, Seok-Hee Hong
UMAP is a popular neighborhood-preserving dimension reduction (DR) algorithm. However, its application for graph drawing has not been evaluated. Moreover, a naive application of UM…
Shape-Faithful Graph Drawings
Amyra Meidiana, Seok-Hee Hong, Peter Eades
Shape-based metrics measure how faithfully a drawing D represents the structure of a graph G, using the proximity graph S of D. While some limited graph classes admit proximity dra…
New Quality Metrics for Dynamic Graph Drawing
Amyra Meidiana, Seok-Hee Hong, Peter Eades
In this paper, we present new quality metrics for dynamic graph drawings. Namely, we present a new framework for change faithfulness metrics for dynamic graph drawings, which compa…
A Quality Metric for Symmetric Graph Drawings
Amyra Meidiana, Seok-Hee Hong, Peter Eades +1
Symmetry is an important aesthetic criteria in graph drawing and network visualisation. Symmetric graph drawings aim to faithfully represent automorphisms of graphs as geometric sy…