---
id: igraph
version: "1.0.0"
license: GNU General Public License (GPL)
license_treatment: copyleft
maintenance: active
---

# igraph — High performance graph data structures and algorithms

License: copyleft · Maintenance: active · Downloads: 3.4M/mo

## What it is and what it does

igraph is a Python binding to a high-performance C graph library designed for network research and complex network analysis. It provides data structures and algorithms for constructing graphs, computing network metrics, detecting communities, and analyzing structural properties. The package is aimed at researchers and developers working with graph-based problems in biology, physics, social networks, and discrete mathematics.

The library's core functionality is written in C for speed, but is exposed through a Python API. Graph visualization is optional and requires separate installation of Cairo bindings (cairocffi or pycairo); the description notes that cairocffi is recommended for Jupyter notebook compatibility. The package supports Python 3.9 and later, with precompiled wheels for most common platforms, reducing installation complexity compared to pure source builds.

Use it for:

- Analyze social networks, citation networks, or biological networks to compute centrality measures and detect community structure
- Perform graph algorithms like shortest paths, spanning trees, or clique detection on large datasets
- Generate publication-quality network visualizations using Cairo integration for research papers or reports
- Study discrete mathematical properties of graphs in academic research or algorithm development
- Build network-based applications requiring fast graph operations on millions of nodes or edges

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

igraph provides a Python interface to a high-performance C graph library for constructing, analyzing, and visualizing networks and complex graphs.

Yes, if you need to work with graphs or networks. igraph is actively maintained, widely used (top 5000 on PyPI), has no known vulnerabilities, and offers substantial performance advantages over pure-Python graph libraries due to its C backend. The copyleft GPL license is a consideration if you plan to redistribute; otherwise it poses no barrier to use. Medium install friction is acceptable given the availability of prebuilt wheels.

## Install

pip install igraph
uv add igraph
poetry add igraph

## Installing igraph

Before you install: Medium install friction due to compiled C bindings; however, prebuilt wheels are available for common platforms (macOS, Linux, Windows) and Python versions 3.9–3.13. The package is actively maintained with recent commits and a stable release cadence.

License in practice: Licensed under GNU General Public License (GPL), a copyleft license. Any software that links to or redistributes igraph must comply with GPL terms, including making source code available under compatible terms.

Quickstart:

pip install igraph

import igraph as ig

# Create a simple graph
g = ig.Graph.Famous("petersen")
print(g.summary())

Requires Python 3.9 or later. Graph plotting requires separate installation of cairocffi or pycairo; cairocffi is recommended for Jupyter compatibility.

Verify before relying:

- Whether graph plotting requires explicit installation of cairocffi or pycairo, or if igraph functions without them
- Performance characteristics and scalability limits for large graphs

## Package facts

- License: GNU General Public License (GPL) (copyleft)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 3.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

graph algorithms and analysis, network analysis library, complex network research, graph data structures, network visualization, graph theory computation, network metrics and properties, graph-algorithms, network-analysis, scientific-computing

[View on SkillFed](https://skillfed.io/packages/igraph) · [View on PyPI](https://pypi.org/project/igraph/)