graspologic
A set of Python modules for graph statistics
Decision gist · record as of 2026-08-14
Yes. Graspologic is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and low installation friction. It is well-suited for anyone working with network or graph data who needs statistical algorithms beyond basic graph operations. The dependency footprint is substantial but standard in scientific Python; if you already use scikit-learn and scipy, adding graspologic is straightforward.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9–3.12 (x86_64 architecture on Linux, macOS, or Windows 10); tested on these platforms only.
- Low friction installation with a pure-Python wheel.
- Active maintenance with a recent release (340 days ago) and ongoing repository activity.
License · maintenance · safety
MIT (permissive) — MIT license is permissive—you can use, modify, and distribute graspologic freely in commercial and private projects with minimal restrictions, only requiring license attribution.
last release 2025-09-08 (340 days) · last repo commit 2026-06-18 · 1,008 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 148,154 downloads/mo, #11,043 on PyPI
Alternatives
Verify before relying
pip install graspologic
import graspologic
from graspologic.embed import AdjacencySpectralEmbed
# Embed a graph adjacency matrix
embedder = AdjacencySpectralEmbed()
embedding = embedder.fit_transform(adjacency_matrix)- Whether the package's graph algorithms scale well to very large networks or if there are documented size/performance limits.
- Whether the embedding and clustering methods are suitable for directed, weighted, or temporal graphs specifically.
- Performance characteristics when working with sparse versus dense adjacency matrices.
What it is and what it does
Graspologic is a Python library for statistical analysis of graphs and networks. It provides algorithms and utilities designed to work with the spatial structure of networks, applying specialized statistical techniques rather than treating graph data as unstructured. The package includes methods for graph embedding, clustering, hypothesis testing, and other analyses that respect the relational structure inherent in network data.
The library depends on a mature scientific Python stack (numpy, scipy, scikit-learn, networkx, matplotlib, seaborn) and is actively maintained with support for Python 3.9 through 3.12. It is intended for researchers and practitioners working with network data in domains like social networks, biological networks, or any domain where relationships between entities matter. The package is production-stable and has been in active development since 2020.
Use it for
- Embed graph structures into low-dimensional vector spaces for downstream machine learning tasks.
- Test statistical hypotheses about network structure, connectivity, or community organization.
- Cluster nodes or detect communities within networks using graph-aware algorithms.
- Analyze and compare multiple networks to identify structural differences or similarities.
- Preprocess and visualize network data for exploratory analysis or publication.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Graspologic is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and low installation friction. It is well-suited for anyone working with network or graph data who needs statistical algorithms beyond basic graph operations. The dependency footprint is substantial but standard in scientific Python; if you already use scikit-learn and scipy, adding graspologic is straightforward.
Install
graspologic on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance with a recent release (340 days ago) and ongoing repository activity. Depends on 17 runtime packages including heavy scientific libraries (scikit-learn, scipy, networkx, gensim), which are standard in the data science ecosystem.
Requires Python 3.9–3.12 (x86_64 architecture on Linux, macOS, or Windows 10); tested on these platforms only.
License in practice
MIT license is permissive—you can use, modify, and distribute graspologic freely in commercial and private projects with minimal restrictions, only requiring license attribution.
Quickstart
pip install graspologic
import graspologic
from graspologic.embed import AdjacencySpectralEmbed
# Embed a graph adjacency matrix
embedder = AdjacencySpectralEmbed()
embedding = embedder.fit_transform(adjacency_matrix)Verify before relying
- Whether the package's graph algorithms scale well to very large networks or if there are documented size/performance limits.
- Whether the embedding and clustering methods are suitable for directed, weighted, or temporal graphs specifically.
- Performance characteristics when working with sparse versus dense adjacency matrices.
Package facts
| License | MIT permissive |
| Python support | Capped below the current Python release <3.13,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 17 packagesPOTanytreebeartypefuturegensimgraspologic-nativehyppojoblibmatplotlibnetworkxnumpyscikit-learnscipyseabornstatsmodelstyping-extensionsumap-learn |
| Maintenance | Actively maintained 340 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 148,154 / month, #11,043 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Mathematics |
Evidence: graspologic-3.4.4-py3-none-any.whl
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See also graspologic-native · scikit-network · grandalf · pointpats · krippendorff · graphframes · cityseer · python-igraph · igraph