---
id: scikit-network
version: "0.33.5"
license: BSD License
license_treatment: permissive
maintenance: active
---

# scikit-network — Graph algorithms

License: permissive · Maintenance: active · Downloads: 1.1M/mo

## 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 above — verify before relying.

scikit-network provides graph algorithms and analysis tools for Python, representing graphs as sparse matrices and offering a scikit-learn-inspired API for machine learning on network data.

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

pip install scikit-network
uv add scikit-network
poetry add scikit-network

## Installing scikit-network

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.

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 library

Requires Python 3.10 or later; compiled extensions require a compatible C/C++ toolchain on some platforms.

Verify 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_current
- Install friction: medium
- Maintenance: active
- Downloads: 1.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

graph algorithms python, network analysis library, graph machine learning, sparse matrix graphs, network science python, community detection, graph analysis, graph-algorithms, network-analysis, sparse-matrices

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