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.
CITATION STYLE
Eades, P., Nguyen, Q., & Hong, S. H. (2018). Drawing big graphs using spectral sparsification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10692 LNCS, pp. 272–286). Springer Verlag. https://doi.org/10.1007/978-3-319-73915-1_22
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