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Found 3 papers, 3 papers with code
Bayesian Hyperbolic Multidimensional Scaling
We propose a Bayesian approach to multidimensional scaling when the low-dimensional manifold is hyperbolic.
Spectral goodness-of-fit tests for complete and partial network data
In this work, we address the question of how to determine whether a parametric model, such as a stochastic block model or latent space model, fits a dataset well and will extrapolate to similar data.
Identifying the latent space geometry of network models through analysis of curvature
A common approach to modeling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood.
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