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 56 articles )

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  1. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  2. Benjamin R. Fitzpatrick, Kerrie Mengersen: A network flow approach to visualising the roles of covariates in random forests (2017) arXiv
  3. 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
  4. Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
  5. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  6. Deisy Morselli Gysi, Andre Voigt, Tiago de Miranda Fragoso, Eivind Almaas, Katja Nowick: wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool (2017) arXiv
  7. Gross, Elizabeth; Petrović, Sonja; Stasi, Despina: Goodness of fit for log-linear network models: dynamic Markov bases using hypergraphs (2017)
  8. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  9. Kaplan, Andee; Hofmann, Heike; Nordman, Daniel: An interactive graphical method for community detection in network data (2017)
  10. Pariya Behrouzi, Ernst C. Wit: netgwas: An R Package for Network-Based Genome-Wide Association Studies (2017) arXiv
  11. Sharma, Rohan; Adhikari, Bibhas; Mishra, Abhishek: Structural and spectral properties of corona graphs (2017)
  12. 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
  13. Bar-Hen, Avner; Poggi, Jean-Michel: Influence measures and stability for graphical models (2016)
  14. Clemente, Gian Paolo; Cornaro, Alessandra: Bounding the $HL$-index of a graph: a majorization approach (2016)
  15. Ioanna Manolopoulou, Axel Hille: BPEC: An R Package for Bayesian Phylogeographic and Ecological Clustering (2016) arXiv
  16. Malmros, J.; Liljeros, F.; Britton, T.: Respondent-driven sampling and an unusual epidemic (2016)
  17. Matthew Friedlander: The Bayesian analysis of contingency table data using the bayesloglin R package (2016) arXiv
  18. Miecznikowski, Jeffrey C.; Gaile, Daniel P.; Chen, Xiwei; Tritchler, David L.: Identification of consistent functional genetic modules (2016)
  19. Noureddine, Mohammad A.; Fawaz, Ahmed; Sanders, William H.; Başar, Tamer: A game-theoretic approach to respond to attacker lateral movement (2016)
  20. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Graph centrality based prediction of cancer genes (2016)

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