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 40 articles )

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  1. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  2. 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
  3. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  4. Gross, Elizabeth; Petrović, Sonja; Stasi, Despina: Goodness of fit for log-linear network models: dynamic Markov bases using hypergraphs (2017)
  5. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  6. Thong Pham, Paul Sheridan, Hidetoshi Shimodaira: PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks (2017) arXiv
  7. Bar-Hen, Avner; Poggi, Jean-Michel: Influence measures and stability for graphical models (2016)
  8. Clemente, Gian Paolo; Cornaro, Alessandra: Bounding the $HL$-index of a graph: a majorization approach (2016)
  9. Matthew Friedlander: The Bayesian analysis of contingency table data using the bayesloglin R package (2016) arXiv
  10. Barbillon, Pierre; Thomas, Mathieu; Goldringer, Isabelle; Hospital, Frédéric; Robin, Stéphane: Network impact on persistence in a finite population dynamic diffusion model: application to an emergent seed exchange network (2015)
  11. Jonathan F. Donges, Jobst Heitzig, Boyan Beronov, Marc Wiedermann, Jakob Runge, Qing Yi Feng, Liubov Tupikina, Veronika Stolbova, Reik V. Donner, Norbert Marwan, Henk A. Dijkstra, J. Kurths: Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package (2015) arXiv
  12. Ma, Tinghuai; Zhang, Yuliang; Cao, Jie; Shen, Jian; Tang, Meili; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Rodhaan, Mznah: KDVEM: a $k$-degree anonymity with vertex and edge modification algorithm (2015)
  13. Mohammadi, A.; Wit, E.C.: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models (2015) arXiv
  14. Nunes, Davide; Antunes, Luis: Modelling structured societies: a multi-relational approach to context permeability (2015)
  15. Rossi, Ryan A.; Gleich, David F.; Gebremedhin, Assefaw H.: Parallel maximum clique algorithms with applications to network analysis (2015)
  16. Duncan, A.J.; Gunn, G.J.; Umstatter, C.; Humphry, R.W.: Replicating disease spread in empirical cattle networks by adjusting the probability of infection in random networks (2014)
  17. Duncan, Melissa; Gu, Wei; He, Yang-Hui; Zhou, Da: The statistics of vacuum geometry (2014)
  18. Fontana, Roberto: Random Latin squares and Sudoku designs generation (2014)
  19. Kolaczyk, Eric D.; Csárdi, Gábor: Statistical analysis of network data with R (2014)
  20. Kraus, Veronika; Dehmer, Matthias; Emmert-Streib, Frank: Probabilistic inequalities for evaluating structural network measures (2014)

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