igraph

The igraph software package for complex network research. igraph is a free software package for creating and manipulating undirected and directed graphs. It includes implementations for classic graph theory problems like minimum spanning trees and network flow, and also implements algorithms for some recent network analysis methods, like community structure search. The efficient implementation of igraph allows it to handle graphs with millions of vertices and edges. The rule of thumb is that if your graph fits into the physical memory then igraph can handle it.


References in zbMATH (referenced in 98 articles )

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  1. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  2. Ansmann, Gerrit: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE (2018)
  3. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  4. Boyd, Zachary M.; Bae, Egil; Tai, Xue-Cheng; Bertozzi, Andrea L.: Simplified energy landscape for modularity using total variation (2018)
  5. Devijver, Emilie; Gallopin, Mélina: Block-diagonal covariance selection for high-dimensional Gaussian graphical models (2018)
  6. Embrechts, P.; Kirchner, M.: Hawkes graphs (2018)
  7. Fairbrother, Jamie; Letchford, Adam N.; Briggs, Keith: A two-level graph partitioning problem arising in mobile wireless communications (2018)
  8. Goerigk, Marc; Hamacher, Horst W.; Kinscherff, Anika: Ranking robustness and its application to evacuation planning (2018)
  9. Jin Zhu, Wenliang Pan, Wei Zheng, Xueqin Wang: Ball: An R package for detecting distribution difference and association in metric spaces (2018) arXiv
  10. Mair, Patrick: Modern psychometrics with R (2018)
  11. Palowitch, John; Bhamidi, Shankar; Nobel, Andrew B.: Significance-based community detection in weighted networks (2018)
  12. Pirim, Harun; Eksioglu, Burak; Glover, Fred W.: A novel mixed integer linear programming model for clustering relational networks (2018)
  13. van der Pas, S. L.; van der Vaart, A. W.: Bayesian community detection (2018)
  14. Vitelli, Valeria; Sørensen, Øystein; Crispino, Marta; Frigessi, Arnoldo; Arjas, Elja: Probabilistic preference learning with the Mallows rank model (2018)
  15. Álvarez-Miranda, Eduardo; Sinnl, Markus: A relax-and-cut framework for large-scale maximum weight connected subgraph problems (2017)
  16. Arcagni, Alberto; Grassi, Rosanna; Stefani, Silvana; Torriero, Anna: Higher order assortativity in complex networks (2017)
  17. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  18. Benjamin R. Fitzpatrick, Kerrie Mengersen: A network flow approach to visualising the roles of covariates in random forests (2017) arXiv
  19. Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou: SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning (2017) arXiv
  20. Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv

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