Drawing Big Graphs Using Spectral Sparsification [chapter]

Peter Eades, Quan Nguyen, Seok-Hee Hong
2018 Lecture Notes in Computer Science  
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.
doi:10.1007/978-3-319-73915-1_22 fatcat:rp7dtzl4hjczbnhlgt5ojsmgje