# Drawing Big Graphs using Spectral Sparsification

arXiv: https://arxiv.org/abs/1708.08659 · HTML: https://mlsanity.com/paper/1708.08659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning](https://mlsanity.com/paper/2410.10875/) (2024)

## Abstract (arXiv)

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.