Installation#

Quick install#

Install from PyPI:

pip install nx-backbone

Upgrading from networkx-backbone#

Versions 0.2 and earlier were published as networkx-backbone and imported as networkx_backbone. From 0.3.0 the package is nx-backbone, imported as nx_backbone, with no alias for the old name. Replace the distribution, then change the imports in your code:

pip uninstall networkx-backbone
pip install nx-backbone

Function names and arguments are unchanged. See the Changelog for the other changes in 0.3.0.

Full installation#

For full functionality, install with optional dependencies. NumPy and SciPy are needed for statistical methods (marginal_likelihood_filter(), ecm_filter()), bipartite methods (sdsm(), fdsm()), doubly_stochastic_filter(), local_path_index(), and KS measures (ks_degree(), ks_weight()), and statistically validated hypergraphs (statistically_validated_hypergraph()):

pip install "nx-backbone[full]"

The plotting helpers in nx_backbone.visualization need Matplotlib:

pip install "nx-backbone[viz]"

Calling a function whose optional dependency is missing raises ImportError; everything else keeps working with NetworkX alone.

pandas is optional and imported only when used: for data-frame input to to_graph() and the bipartite functions, for the event tables of from_event_table() and to_event_table(), and for frequency strings such as freq="1D" in the temporal module. Install it separately:

pip install pandas

transform_weights() accepts scikit-learn transformers by duck typing, so scikit-learn is never required.

Hypergraph interoperability#

The from_*/to_* adapters in nx_backbone.hypergraph_io import their target library lazily, so none of them is a dependency. Install the ones you use through an extra, or all four with [interop]:

pip install "nx-backbone[xgi]"          # or [hypernetx], [hypergraphx], [hat]
pip install "nx-backbone[interop]"      # all four

Reading and writing the HIF interchange format (read_hif(), write_hif()) needs only the standard library.

The version floors below are the oldest releases that pass the round-trip tests in tests/test_hypergraph_interop.py: hyperedges, weights, and node labels survive from_X(to_X(h)) and to_X(from_X(H)), and each library’s own HIF reader and writer exchange files with ours. The “Interop” CI workflow runs these tests against the latest releases on every pull request and weekly; it is not a required check, so an upstream release cannot block unrelated work.

Extra	PyPI distribution (import name)	Version	Notes
`[xgi]`	`xgi`	>= 0.9.1	Multi-edges kept. xgi’s HIF reader ignores the `weight` field of edge records, so weights read with `xgi.read_hif` are lost; use `to_xgi(*read_hif(path, return_weights=True))`.
`[hypernetx]`	`hypernetx`	>= 2.3.10	HIF support (`hnx.to_hif`/`hnx.from_hif`) first appears in 2.3.10; the adapters themselves also work with 2.3.2. Weights come back as floats.
`[hypergraphx]`	`hypergraphx`	>= 1.7.7	No multi-edges: `to_hypergraphx()` merges a repeated hyperedge into one edge with the summed weight. Node labels in a hyperedge must be mutually comparable (HypergraphX sorts them). Its HIF reader relabels nodes `0..n-1` and ignores weights, and its HIF writer (1.8.0) leaves out the incidences.
`[hat]`	`HypergraphAnalysisToolbox` (`HAT`)	>= 1.1.18	Building from an incidence matrix fails in 1.1.14 and 1.1.16. `to_hat()` stores node labels in `H.nodes["Names"]` and weights in `H.edges["weight"]`.

These ranges were tested on Python 3.13 with xgi 0.10.2, HyperNetX 2.4.3, HypergraphX 1.8.0, and HAT 1.1.18 (the latest releases in November 2026) and with the floors above.

Conda installation#

Install from anaconda.org:

conda install -c brianckeegan nx-backbone

Development installation#

Clone the repository and install in editable mode:

git clone https://github.com/brianckeegan/nx-backbone.git
cd nx-backbone
pip install -e ".[dev]"

Run the test suite (including docstring examples) to verify the installation:

pytest --doctest-modules nx_backbone tests

Requirements#

Supported versions follow NetworkX’s: Python 3.12 or newer, and the same minimum versions of NumPy, SciPy, and Matplotlib. CI tests these minimums exactly, along with the latest releases.

Package	Version	Status
Python	>= 3.12	Required
NetworkX	>= 3.4	Required
NumPy	>= 2.2	Optional (for `[full]`)
SciPy	>= 1.15	Optional (for `[full]`)
Matplotlib	>= 3.10	Optional (for `[viz]`)
pandas	>= 2.3	Optional (data-frame input, event tables, frequency strings)