Requirements

Python version

Python 3.9 or newer is required.

Base install

pip install spatial_graph_algorithms

Installs everything needed for simulation, MDS/STRND reconstruction, metrics, and plotting.

Package	Version	Used for
`numpy`	≥ 1.21	Array operations throughout
`scipy`	≥ 1.7	Sparse matrices, shortest paths, Procrustes alignment
`pandas`	≥ 1.3	Edge/node metadata DataFrames
`networkx`	≥ 2.8	Graph operations, clustering coefficient
`python-igraph`	≥ 0.10	Fast graph conversion (`to_igraph`)
`scikit-learn`	≥ 1.0	MDS, nearest neighbours
`matplotlib`	≥ 3.5	All plotting functions
`Pillow`	≥ 9.0	Bundled shape/image loading
`pecanpy`	≥ 2.0	Node2Vec biased random walks for STRND
`umap-learn`	≥ 0.5	Dimensionality reduction for STRND

Optional extras

Compatibility note: Two runtime shims are applied automatically when STRND is first called, patching known incompatibilities between pecanpy/gensim and newer scipy/sklearn. No user action is needed.

Leiden community detection

pip install "spatial_graph_algorithms[leiden]"

Package	Version	Used for
`leidenalg`	≥ 0.9	Community-based edge filtering

GSE reconstruction

pip install "spatial_graph_algorithms[gse]"

Required for reconstruct(sg, method="gse").

Package	Version	Used for
`annoy`	≥ 1.17	Approximate nearest-neighbour backend
`faiss-cpu`	≥ 1.7	FAISS approximate nearest-neighbour backend
`pymetis`	≥ 2023.1	Graph partitioning backend
`numba`	≥ 0.57	JIT acceleration

All optional features

pip install "spatial_graph_algorithms[all]"

Installs Leiden + GSE + PyTorch + PyMDE + PaCMAP and optional acceleration / embedding backends.

Package	Version	Used for
`torch`	≥ 2.0	Optional embedding backends
`pymde`	≥ 0.1	Optional manifold embedding experiments
`pacmap`	≥ 0.7	Optional manifold embedding experiments
`annoy`	≥ 1.17	Approximate nearest-neighbour backend
`faiss-cpu`	≥ 1.7	FAISS approximate nearest-neighbour backend
`pymetis`	≥ 2023.1	Graph partitioning backend

Development install

git clone https://github.com/DavidFernandezBonet/spatial-graph-algorithms
cd spatial-graph-algorithms
pip install -e ".[dev]"
pytest tests/

The dev extra adds pytest, pytest-cov, and ruff.

Operating systems

Tested on Linux and macOS. Windows should work but is not explicitly tested.

The base package has no compiled C extensions. Some optional extras, including faiss-cpu and pymetis under [all], install compiled wheels.

Checking your install

import spatial_graph_algorithms
print(spatial_graph_algorithms.__version__)

from spatial_graph_algorithms.simulate import generate
from spatial_graph_algorithms.reconstruct import reconstruct
from spatial_graph_algorithms.metrics import evaluate

sg     = generate(n=100, seed=42)
sg_rec = reconstruct(sg, method="mds", seed=42)
m      = evaluate(sg_rec)
print("CPD:", m["cpd"])   # should be > 0.5