PyBiRewireX
Python port of the BiRewire R package — fast degree-preserving network randomisation via the Switching Algorithm with NetworkX and igraph support.
Given a binary network (bipartite or undirected), PyBiRewireX generates rewired versions that are uniformly drawn from the space of all networks with the same degree sequence. This null model can be used to test whether observed network properties are trivially explained by degree alone.
Installation
pip install pybirewirex
With optional graph-object support (igraph / networkx):
pip install "pybirewirex[graph]"
Requirements: Python ≥ 3.10, a C compiler (for building the extension). Pre-built wheels are available for Linux x86_64/aarch64, macOS x86_64/arm64, and Windows x86_64.
Quick start
import numpy as np
import pybirewirex as pbr
# Any binary 2-D ndarray (bipartite incidence matrix)
matrix = (np.random.default_rng(0).random((100, 40)) < 0.2).astype(np.int16)
# Rewire — row and column sums preserved
rewired = pbr.rewire_bipartite(matrix, seed=42)
assert np.array_equal(matrix.sum(axis=1), rewired.sum(axis=1)) # row sums
assert np.array_equal(matrix.sum(axis=0), rewired.sum(axis=0)) # col sums
Undirected graphs work identically — pass a symmetric adjacency matrix:
rewired_graph = pbr.rewire_undirected(adjacency, seed=42)
scipy sparse matrices, igraph graphs, and networkx graphs are accepted transparently and returned in the same format.
Convergence analysis
The analytical bound N gives the minimum number of switching steps needed to
reach the stationary distribution. analysis_bipartite tracks Jaccard
similarity at every step iterations across n_networks independent runs,
letting you verify that the chain has converged.
result = pbr.analysis_bipartite(
matrix,
n_networks = 10, # independent rewiring runs
step = 1000, # record Jaccard every 1 000 steps
seed = 0,
)
print(f"Analytical bound N = {result.N}")
print(f"Scores shape: {result.scores.shape}") # (10, n_steps)
R vignette — Python reproductions
The figures below reproduce the two main outputs from the BiRewire R package
vignette (BiRewire.Rnw), generated by scripts/demo.py.
Analysis — birewire.analysis.bipartite
Jaccard similarity between the original network and its rewired snapshots,
sampled every step switching steps across n_networks independent runs.
The red line marks the analytical bound N.
Original BiRewire (R):
PyBiRewireX (Python):
Monitoring — birewire.visual.monitoring.bipartite
t-SNE visualisation of the Markov chain underlying the Switching Algorithm.
Each point is a sampled network; colour encodes sampling order (blue = first,
red = last). At small step counts the chain moves slowly (tight clusters); at
k = N and beyond the samples are uniformly distributed (scattered cloud).
Original BiRewire (R):
PyBiRewireX (Python):
API reference
| Function | Description |
|---|---|
rewire_bipartite(matrix, ...) |
Rewire bipartite network, preserving row and column sums |
rewire_undirected(adjacency, ...) |
Rewire undirected graph, preserving degree sequence |
analysis_bipartite(matrix, ...) |
Convergence analysis for bipartite rewiring |
analysis_undirected(adjacency, ...) |
Convergence analysis for undirected rewiring |
AnalysisResult |
Dataclass: .N (bound), .scores (n_networks × n_steps), .step |
All rewiring functions share the same keyword arguments:
| Argument | Type | Default | Description |
|---|---|---|---|
max_iter |
int | "n" |
"n" |
Switching steps; "n" auto-computes bound N |
accuracy |
float |
1e-5 |
Target accuracy δ for bound formula |
exact |
bool |
False |
Use exact vs approximate bound formula |
verbose |
bool |
True |
Print progress to stderr |
seed |
int | None |
None |
RNG seed (None = random) |
Full API docs at pybirewirex.readthedocs.io. WORK IN PROGRES!!!
Development
git clone https://github.com/lorenzoamir/PyBiRewire
cd PyBiRewire
pip install -e ".[dev]"
pytest # run test suite
ruff check . # lint
ruff format . # format
Build the Jupyter tutorial notebook:
jupyter notebook notebooks/tutorial.ipynb
Build docs locally:
cd docs && make html
Citation
If you use PyBiRewireX in published research, please cite the original BiRewire paper:
Iorio, F., Bernardo-Faura, M., Gobbi, A., Cokelaer, T., Jurman, G. and Saez-Rodriguez, J., Efficient randomization of biological networks while preserving functional characterization of individual nodes. BMC Bioinformatics, 17(1), p.542. (2016). http://doi.org/10.1186/s12859-016-1402-1
License
GPL-3.0-or-later — see LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for pybirewirex 0.2.2
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