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.
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2 Pith papers cite this work. Polarity classification is still indexing.
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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 inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.
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Making Uncertainty Visible: Multiverse Analysis for Robust Computational Social Science
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.