scikit-network
Graph algorithms
Decision gist · record as of 2026-08-14
Yes. scikit-network is actively maintained with no known vulnerabilities, supports current Python versions (3.10–3.14), and offers a mature, well-documented API for graph analysis. Medium install friction is manageable with pre-built wheels across major platforms. Suitable for research, production graph analysis, and machine-learning workflows involving network data.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; compiled extensions require a compatible C/C++ toolchain on some platforms.
- Medium install friction due to compiled wheels, but well-supported across Python 3.10–3.14 and major platforms (macOS, Linux, Windows).
- Active maintenance with recent commits and no known vulnerabilities.
License · maintenance · safety
BSD License (permissive) — BSD License (permissive) allows commercial and private use with minimal restrictions; you may use and modify freely as long as you retain the license notice.
last release 2025-11-19 (268 days) · last repo commit 2026-07-02 · 633 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,109,468 downloads/mo, #4,357 on PyPI
Alternatives
Verify before relying
pip install scikit-network
import scikit-network
# Load or create a graph and apply algorithms from the library- Specific graph algorithms available and their computational complexity or scalability limits.
- Whether the library supports directed, undirected, weighted, or all graph types.
- Performance benchmarks or typical throughput for real-world network datasets.
- Detailed feature set beyond memory-efficient sparse matrix representation and fast algorithms.
What it is and what it does
scikit-network is a Python library for analyzing and computing on graph-structured data, using memory-efficient sparse matrix representations compatible with scipy. It combines fast algorithms with an API modeled after scikit-learn, making it accessible to users familiar with the broader Python machine-learning ecosystem. The library depends on numpy and scipy for its numerical and sparse-matrix foundations.
The package is actively maintained, supports Python 3.10–3.14, and is published under a permissive BSD License. It has been in development since 2018 and is cited in peer-reviewed literature, indicating established use in research and production settings. Medium install friction is offset by comprehensive platform coverage with pre-built wheels.
Use it for
- Analyze community structure or clustering within social networks and collaboration graphs.
- Compute node centrality and importance rankings in large network datasets.
- Process citation networks, co-authorship graphs, or academic collaboration structures.
- Incorporate graph topology as features in machine-learning pipelines.
- Perform link prediction or graph embedding on sparse real-world networks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
scikit-network is actively maintained with no known vulnerabilities, supports current Python versions (3.10–3.14), and offers a mature, well-documented API for graph analysis. Medium install friction is manageable with pre-built wheels across major platforms. Suitable for research, production graph analysis, and machine-learning workflows involving network data.
Install
scikit-network on PyPI
Before you install
Medium install friction due to compiled wheels, but well-supported across Python 3.10–3.14 and major platforms (macOS, Linux, Windows). Active maintenance with recent commits and no known vulnerabilities.
Requires Python 3.10 or later; compiled extensions require a compatible C/C++ toolchain on some platforms.
License in practice
BSD License (permissive) allows commercial and private use with minimal restrictions; you may use and modify freely as long as you retain the license notice.
Quickstart
pip install scikit-network
import scikit-network
# Load or create a graph and apply algorithms from the libraryVerify before relying
- Specific graph algorithms available and their computational complexity or scalability limits.
- Whether the library supports directed, undirected, weighted, or all graph types.
- Performance benchmarks or typical throughput for real-world network datasets.
- Detailed feature set beyond memory-efficient sparse matrix representation and fast algorithms.
Package facts
| License | BSD License permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Actively maintained 268 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,109,468 / month, #4,357 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseNatural Language :: EnglishProgramming Language :: CythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: scikit_network-0.33.5-cp310-cp310-macosx_10_9_x86_64.whl; scikit_network-0.33.5-cp310-cp310-macosx_11_0_arm64.whl; scikit_network-0.33.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; scikit_network-0.33.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; scikit_network-0.33.5-cp310-cp310-win_amd64.whl; scikit_network-0.33.5-cp311-cp311-macosx_10_9_x86_64.whl; scikit_network-0.33.5-cp311-cp311-macosx_11_0_arm64.whl; scikit_network-0.33.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; scikit_network-0.33.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; scikit_network-0.33.5-cp311-cp311-win_amd64.whl; scikit_network-0.33.5-cp312-cp312-macosx_10_13_x86_64.whl; scikit_network-0.33.5-cp312-cp312-macosx_11_0_arm64.whl; scikit_network-0.33.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; scikit_network-0.33.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; scikit_network-0.33.5-cp312-cp312-win_amd64.whl; scikit_network-0.33.5-cp313-cp313-macosx_10_13_x86_64.whl; scikit_network-0.33.5-cp313-cp313-macosx_11_0_arm64.whl; scikit_network-0.33.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; scikit_network-0.33.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; scikit_network-0.33.5-cp313-cp313-win_amd64.whl
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