nx-rustworkx

A NetworkX 3.x backend that accelerates selected graph algorithms with rustworkx.

Keep import networkx as nx. nx-rustworkx converts an nx.Graph, runs the rustworkx kernel, and remaps the result to the original node IDs. Unsupported calls stay on NetworkX.

How much faster?

Real NetworkX projects with the backend switched on, application code unchanged — measured by the runners in benches/external/ on one 4-CPU machine:

Good fit: CPU-heavy whole-graph algorithms on graphs from a few hundred nodes up — centralities, all-pairs shortest paths, components, isomorphism — and repeat-call pipelines, where the one-time conversion is cached across calls. Street networks work as-is: MultiDiGraphs with parallel ways dispatch with NetworkX's parallel-edge semantics.

Not the tool: tiny graphs (auto-dispatch declines below 200 nodes or 400 edges on purpose), one-off calls to linear-time functions where conversion costs more than NetworkX's answer, algorithms NetworkX already runs on C-backed SciPy (pagerank), custom weight callables, and code that walks G.adj itself instead of calling nx.* functions — a backend can only accelerate the NetworkX API.

Install

pip install nx-rustworkx

Requires Python 3.10+, NetworkX 3.4+, and rustworkx 0.18+.

Use

Set rustworkx as a preferred backend:

NETWORKX_BACKEND_PRIORITY=rustworkx python your_script.py

Or configure it in Python:

import networkx as nx

G = nx.erdos_renyi_graph(2_000, 0.01, seed=1)
nx.config.backend_priority = ["rustworkx"]

scores = nx.betweenness_centrality(G)

The backend implements 111 algorithms. Its measured cutoffs keep small or conversion-heavy calls on NetworkX; backend="rustworkx" explicitly tries the rustworkx implementation.

Generators such as nx.path_graph and nx.gnp_random_graph can construct rustworkx-backed graphs directly, so whole pipelines skip conversion; the usage guide covers enabling generator dispatch.

Documentation

Usage, configuration, supported algorithms, caveats, and benchmarks: ville.dev/nx-rustworkx

Limits

• MultiGraph and MultiDiGraph dispatch with NetworkX's parallel-edge semantics (minimum weight for paths, summed weights for PageRank, collapsed bundles for betweenness and bridges, keyed results for spanning trees). The 14 functions NetworkX itself refuses on multigraphs, plus complement, the graph products and vf2pp_all_isomorphisms, fall back to NetworkX; so do the native generators when create_using is a multigraph class.
• No custom weight callables.
• The rustworkx-backed graph object does not implement drawing or I/O.
• Some valid results may differ in ordering or floating-point rounding.
• Seeded random generators reproduce NetworkX's graphs unless native_seeded_generators is enabled; the opt-in draws from rustworkx's RNG, so the same seed gives a different, equally valid graph.

The algorithm reference lists the exact behavior and fallback conditions.

Development

uv sync --extra test
uv run pytest tests

See the development guide for the full test, lint, benchmark, and architecture notes.

License

BSD-3-Clause.