Bayesian latent space models for graphs are misspecified on real data, leading to poor calibration; a new generalized posterior with adaptive regularization via prequential risk estimation improves performance and geometry choice.
Physical Review E—Statistical, Nonlinear, and Soft Matter Physics , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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A Bayesian hyperbolic latent space model with inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.
citing papers explorer
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Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
Bayesian latent space models for graphs are misspecified on real data, leading to poor calibration; a new generalized posterior with adaptive regularization via prequential risk estimation improves performance and geometry choice.
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Hyperbolic Latent Space Models for Network Embedding: Model Specification and Bayesian Inference
A Bayesian hyperbolic latent space model with inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.