Spectral Sparsification of Graphs
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SNAW01  Graph limits and statistics
Modern graph algorithms increasingly access and manipulate graphs via their Laplacian operators and other associated “continuous” objects, rather than purely discretely. An important primitive in this paradigm is spectral sparsification: being able to approximate the Laplacian of a given graph by that of a graph with significantly fewer edges. I will survey some of the key results in this area, drawing on tools from random matrix theory, matrix analysis, and electrical network theory.
This talk is part of the Isaac Newton Institute Seminar Series series.
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