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Peer-reviewedCC BYPLoS ONE

igraph 1.0 enables fast and robust network analysis across programming languages

  • Michael Antonov1
  • Gábor Csárdi2
  • Szabolcs Horvát3,4
  • Kirill Müller1
  • Tamás Nepusz5
  • Daniel Noom6
  • Maëlle Salmon1
  • Vincent Traag7
  • Brooke Foucault Welles8
  • Fabio Zanini9,10
10 affiliations
  1. 1cynkra GmbH., Zürich, Switzerland
  2. 2Posit Software, PBC, Boston, Massachusetts, United States of America
  3. 3Center for Systems Biology Dresden, Dresden, Germany
  4. 4Reykjavik University, Reykjavík, Iceland
  5. 5Sixdegrees Ltd, Budapest, Hungary
  6. 6jitjit, Amsterdam, Netherlands
  7. 7Centre for Science and Technology Studies, Leiden University, Leiden, Zuid-Holland, Netherlands
  8. 8Northeastern University, Boston, Massachusett, United States of America
  9. 9Evolution & Ecology Research Centre, UNSW Sydney, Sydney, NSW, Australia
  10. 10School of Clinical Medicine, UNSW Sydney, Sydney, NSW, Australia
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Abstract

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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Cite

BibTeXRIS
BibTeX
@article{antonov2026igraph,
  title = {{igraph 1.0 enables fast and robust network analysis across programming languages}},
  author = {Michael Antonov and Gábor Csárdi and Szabolcs Horvát and Kirill Müller and Tamás Nepusz and Daniel Noom and Maëlle Salmon and Vincent Traag and Brooke Foucault Welles and Fabio Zanini},
  journal = {PLoS ONE},
  year = {2026},
  month = aug,
  doi = {10.1371/journal.pone.0355108},
  url = {https://doi.org/10.1371/journal.pone.0355108},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - igraph 1.0 enables fast and robust network analysis across programming languages
AU  - Michael Antonov
AU  - Gábor Csárdi
AU  - Szabolcs Horvát
AU  - Kirill Müller
AU  - Tamás Nepusz
AU  - Daniel Noom
AU  - Maëlle Salmon
AU  - Vincent Traag
AU  - Brooke Foucault Welles
AU  - Fabio Zanini
PY  - 2026
DA  - 2026/08/04
JO  - PLoS ONE
DO  - 10.1371/journal.pone.0355108
UR  - https://doi.org/10.1371/journal.pone.0355108
AB  - 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.
SN  - 1932-6203
ER  -