Probability density estimation for sets of large graphs with respect to spectral information using stochastic block models
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For graph-valued data sampled iid from a distribution μ, the sample moments are computed with respect to a choice of metric. In this work, we equip the set of graphs with the pseudo-metric defined by the ℓ_2 norm between the eigenvalues of the respective adjacency matrices. We use this pseudo metric and the respective sample moments of a graph valued data set to infer the parameters of a distribution μ̂ and interpret this distribution as an approximation of μ. We verify experimentally that complex distributions μ can be approximated well taking this approach.
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