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.
Physical Review E—Statistical, Nonlinear, and Soft Matter Physics , volume=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.