igraph enables fast and robust network analysis across programming languages

TL;DR

igraph is a versatile, high-performance library for network analysis that supports multiple programming languages and has been extensively expanded and improved over two decades to enhance scalability, robustness, and community engagement.

Contribution

The paper introduces igraph's recent developments, including scalability to billions of edges, multi-language support, new algorithms, and community-focused features, making it a comprehensive network analysis tool.

Findings

Supports billions of edges efficiently

Integrates with Jupyter and other tools

Enhanced robustness and community engagement

Abstract

Networks or graphs are widely used across the sciences to represent relationships of many kinds. igraph (https://igraph.org) is a general-purpose software library for graph construction, analysis, and visualisation, combining fast and robust performance with a low entry barrier. igraph pairs a fast core written in C with beginner-friendly interfaces in Python, R, and Mathematica. Over the last two decades, igraph has expanded substantially. It now scales to billions of edges, supports Mathematica and interactive plotting, integrates with Jupyter notebooks and other network libraries, includes new graph layouts and community detection algorithms, and has streamlined the documentation with examples and Spanish translations. Modern testing features such as continuous integration, address sanitizers, stricter typing, and memory-managed vectors have also increased robustness. Hundreds of bug reports have been fixed and a community forum has been opened to connect users and developers. Specific effort has been made to broaden use and community participation by women, non-binary people, and other demographic groups typically underrepresented in open source software.