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- W2970181000 abstract "Random geometric graphs are a popular choice for a latent points generative model for networks. Their definition is based on a sample of $n$ points $X_1,X_2,cdots,X_n$ on the Euclidean sphere~$mathbb{S}^{d-1}$ which represents the latent positions of nodes of the network. The connection probabilities between the nodes are determined by an unknown function (referred to as the ``link'' function) evaluated at the distance between the latent points. We introduce a spectral estimator of the pairwise distance between latent points and we prove that its rate of convergence is the same as the nonparametric estimation of a function on $mathbb{S}^{d-1}$, up to a logarithmic factor. In addition, we provide an efficient spectral algorithm to compute this estimator without any knowledge on the nonparametric link function. As a byproduct, our method can also consistently estimate the dimension $d$ of the latent space." @default.
- W2970181000 created "2019-09-05" @default.
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- W2970181000 date "2019-01-01" @default.
- W2970181000 modified "2023-10-18" @default.
- W2970181000 title "Latent distance estimation for random geometric graphs" @default.
- W2970181000 hasPublicationYear "2019" @default.
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