pybigue 1.0.0

Sampling algorithm of network hyperbolic embeddings.

BIGUE hyperbolic embedder

Bayesian Inference of a Graph's Unkown Embedding (BIGUE) is a Markov Chain Monte Carlo algorithm that samples the best hyperbolic embeddings of a given network according to a Bayesian $\mathbb{S}^1$ model.

The code used to generate the Figures in the paper is in the bigue-analysis repository. If you use this algorithm, please cite:

Simon Lizotte, Jean-Gabriel Young and Antoine Allard "Symmetry-driven embedding of networks in hyperbolic space". Commun. Phys. 8, 199 (2025). https://doi.org/10.1038/s42005-025-02122-0

Installation

Dependencies

The pybigue library requires:

• Python (>= 3.10)
• NumPy
• SciPy
• CmdStanPy
• Numba

pybigue can be installed directly from PyPi

pip install pybigue

Usage

To use the default BIGUE sampler,

from pybigue.sampling import sample_bigue, read_sample

# ... code that obtains an numpy adjacency matrix

sample_directory = "./sample/"
sample_bigue(adjacency_matrix, sample_directory) # Writes samples to disk
samples = read_sample(sample_directory) # Reads samples

Two complete examples are presented in the examples. The file embed_synthetic_graph.py uses the default BIGUE algorithm (requires also matplotlib) and the file manual_sampling.py shows how one can tweak the sampler (hyperparameter values, prior distributions, subkernel selection probabilities, combining with Stan HMC, etc.).

Note that the samples are not aligned automatically. This Python library doesn't provide a tool to compute automorphisms. This can be found in the bigue-analysis repository. Nevertheless, the samples can be aligned given a list of automorphisms

from pybigue.models import S1Model

# Each automorphism is an array of the new label of each vertex.
automorphisms = [np.arange(adjacency_matrix.shape[0])] # Trivial automorphism
aligned_samples = {
    chain: S1Model.align_sample(sample, reference_thetas=some_thetas, automorphisms=automorphisms)
        for chain, sample in samples.items()
}

Source code

The lastest version can be fetched with

git clone https://github.com/DynamicaLab/bigue.git

Testing

After downloding the code, the test suite can be ran using (requires installing pytest)

pytest pybigue