Spectrum-preserving sparsification for visualization of big graphs
نویسندگان
چکیده
منابع مشابه
Drawing Big Graphs Using Spectral Sparsification
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 effecti...
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We introduce a new notion of graph sparsification based on spectral similarity of graph Laplacians: spectral sparsification requires that the Laplacian quadratic form of the sparsifier approximate that of the original. This is equivalent to saying that the Laplacian of the sparsifier is a good preconditioner for the Laplacian of the original. We prove that every graph has a spectral sparsifier ...
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Cluster analysis technology often grapples with high-dimensional and noisy data. The paper in hand identifies sparsification as an approach to address this problem. Sparsification improves both the runtime and the quality of cluster algorithms that exploit pairwise object similarities, i.e., that rely on similarity graphs. Sparsification has been addressed in the field of graphical cluster algo...
متن کاملSpectral Sparsification of Undirected Graphs
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ژورنال
عنوان ژورنال: Computers & Graphics
سال: 2020
ISSN: 0097-8493
DOI: 10.1016/j.cag.2020.02.004