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Entropic Spectral Learning for Large-Scale Graphs
[article]
2019
arXiv
pre-print
Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network. For large graphs, where an eigen-decomposition is infeasible, iterative moment matched approximations to the spectra and kernel smoothing are typically used. We show that the underlying moment information is lost when using kernel smoothing. We further propose a spectral density approximation based on the method of Maximum Entropy, for
arXiv:1804.06802v2
fatcat:2iax5gllfrfuplsatbl6kn77ae