Drawing Big Graphs using Spectral Sparsification

TL;DR

This paper explores how spectral sparsification can be used to improve the visualization of large graphs by reducing edges while preserving structure, demonstrating its effectiveness over random sampling.

Contribution

It introduces the application of spectral sparsification techniques specifically for big graph visualization and provides evaluation guidelines based on empirical results.

Findings

Spectral sparsifiers outperform random edge sampling in preserving graph structure.

Spectral sparsification leads to clearer visual representations of large graphs.

Guidelines for applying spectral sparsification in big graph visualization are proposed.

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.