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Graph algorithms implemented in Rust, available as a Python package. >10x faster than networkx
.
So far, there is only one function implemented: all_pairs_dijkstra_path_length
. It's a re-write of the networkx
function with the same name and should return the same results.
pip install rust-graph
from rust_graph import all_pairs_dijkstra_path_length
weighted_edges = [
(0, 1, 1.0),
(1, 2, 2.0),
(2, 3, 3.0),
(3, 0, 4.0),
(0, 3, 5.0),
]
shortest_paths = all_pairs_dijkstra_path_length(weighted_edges, cutoff=3.0)
>>> shortest_paths
{3: {3: 0.0, 2: 3.0}, 2: {2: 0.0, 1: 2.0, 0: 3.0, 3: 3.0}, 1: {0: 1.0, 2: 2.0, 1: 0.0}, 0: {1: 1.0, 0: 0.0, 2: 3.0}}
Tried a couple of options but failed for various reasons. Here are some notes on them:
networkx
for the test data.cutoff
parameter is not implemented.Thus, we compare the performance of networkx
and rust-graph
for the all_pairs_dijkstra_path_length
function.
23x as fast as networkx
:
networkx Dijkstra took 4.45 s
rust-graph Dijkstra took 0.19 s
12x as fast as networkx
:
networkx Dijkstra took 6.83 s
rust-graph Dijkstra took 0.57 s
If not using rayon parallelism, it's twice as slow:
networkx Dijkstra took 7.12 s
rust-graph Dijkstra took 1.04 s
CPU info:
Model name: AMD EPYC 7V13 64-Core Processor
CPU family: 25
Model: 1
Thread(s) per core: 1
Core(s) per socket: 48
15x as fast as networkx
:
networkx Dijkstra took 6.14 s
rust-graph Dijkstra took 0.41 s
Install uv
, rustup
and maturin
. Activate a virtual environment. Then,
bash scripts/install.sh
uv pip install -r deps/requirements_dev.in
python3 scripts/hf_download.py # Download test data
python3 tools/benchmark.py
Use GitHub Actions: apply-pip-compile.yml
. Manually launch the workflow and it will make a commit with the updated lockfiles.
FAQs
Simple and fast graph operations written in Rust
We found that rust-graph demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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