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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Multiverse analysis of three published CSS studies reveals substantial variation in findings across methodological decision combinations and identifies cases of computational failure not reported in originals.
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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.