Rooted motif signatures determine latent position connectivity profiles for generic finite-rank graphons and yield empirical estimators with concentration bounds from a single observed graph.
The Annals of Statistics , year =
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A Bayesian framework with adaptive elastic nets and variational EM infers Gaussian graphical models from high-dimensional data with reliable FDR control and good power on heterogeneous graphs.
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Beyond Degree: Rooted Motif Signatures for Latent Position Identifiability in Graphon Models
Rooted motif signatures determine latent position connectivity profiles for generic finite-rank graphons and yield empirical estimators with concentration bounds from a single observed graph.
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A Bayesian framework with adaptive elastic nets for the inference of Gaussian graphical models
A Bayesian framework with adaptive elastic nets and variational EM infers Gaussian graphical models from high-dimensional data with reliable FDR control and good power on heterogeneous graphs.