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.
Advances in neural information processing systems , volume=
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Transfer learning from informative source networks improves target DCMM estimation accuracy by enlarging the eigenvalue gap of the connection probability matrix, with algorithms to avoid negative transfer.
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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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Transfer Learning for Degree-Corrected Mixed Membership Network Models
Transfer learning from informative source networks improves target DCMM estimation accuracy by enlarging the eigenvalue gap of the connection probability matrix, with algorithms to avoid negative transfer.