igraph 1.0 enables fast and robust network analysis across programming languages
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Networks or graphs are widely used across the sciences to represent relationships of many kinds. The igraph (https://igraph.org) software library supports 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. After twenty years of development, igraph 1.0 has been released, enabling public access to a robust and stable network analysis with over a million monthly downloads. Thanks to its cross-language design, igraph delivers both speed and flexibility: it can handle billions of edges, supports interactive plotting, integrates with notebooks, facilitates conversions to and from other network libraries, includes a rich library of graph layout and community detection algorithms, and has a detailed documentation including non-English 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.
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Publisher Copyright: © 2026, Antonov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright: © 2026 Antonov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Programming Languages, Software, Algorithms, Computer Graphics, Humans, Multidisciplinary
Citation
Antonov, M, Csárdi, G, Horvát, S, Müller, K, Nepusz, T, Noom, D, Salmon, M, Traag, V, Welles, B F & Zanini, F 2026, 'igraph 1.0 enables fast and robust network analysis across programming languages', PLoS ONE, vol. 21, no. 8, e0355108, pp. e0355108. https://doi.org/10.1371/journal.pone.0355108