Symmetry-driven embedding of networks in hyperbolic space
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Abstract (arXiv)
Hyperbolic models are known to produce networks with properties observed empirically in most network datasets, including heavy-tailed degree distribution, high clustering, and hierarchical structures. As a result, several embeddings algorithms have been proposed to invert these models and assign hyperbolic coordinates to network data. Current algorithms for finding these coordinates, however, do not quantify uncertainty in the inferred coordinates. We present BIGUE, a Markov chain Monte Carlo (MCMC) algorithm that samples the posterior distribution of a Bayesian hyperbolic random graph model. We show that the samples are consistent with current algorithms while providing added credible intervals for the coordinates and all network properties. We also show that some networks admit two or more plausible embeddings, a feature that an optimization algorithm can easily overlook.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 101 references that are in the map. First posted on arXiv 15 Jun 2024.
Builds on
32 of 101 references.
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20 more
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