A Bayesian hyperbolic latent space model with an inferred temperature parameter outperforms fixed-temperature and Euclidean alternatives in network reconstruction on simulated and real data.
Communications Physics , volume=
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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 an inferred temperature parameter outperforms fixed-temperature and Euclidean alternatives in network reconstruction on simulated and real data.
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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.