Drawing Big Graphs using Spectral Sparsification
Computational Geometry
2017-08-31 v2 Social and Information Networks
Abstract
Spectral sparsification is a general technique developed by Spielman et al. to reduce the number of edges in a graph while retaining its structural properties. We investigate the use of spectral sparsification to produce good visual representations of big graphs. We evaluate spectral sparsification approaches on real-world and synthetic graphs. We show that spectral sparsifiers are more effective than random edge sampling. Our results lead to guidelines for using spectral sparsification in big graph visualization.
Keywords
Cite
@article{arxiv.1708.08659,
title = {Drawing Big Graphs using Spectral Sparsification},
author = {Peter Eades and Quan Nguyen and Seok-Hee Hong},
journal= {arXiv preprint arXiv:1708.08659},
year = {2017}
}
Comments
Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017)