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 89 articles )

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  1. Ansmann, Gerrit: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE (2018)
  2. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  3. Embrechts, P.; Kirchner, M.: Hawkes graphs (2018)
  4. Fairbrother, Jamie; Letchford, Adam N.; Briggs, Keith: A two-level graph partitioning problem arising in mobile wireless communications (2018)
  5. Goerigk, Marc; Hamacher, Horst W.; Kinscherff, Anika: Ranking robustness and its application to evacuation planning (2018)
  6. Mair, Patrick: Modern psychometrics with R (2018)
  7. Pirim, Harun; Eksioglu, Burak; Glover, Fred W.: A novel mixed integer linear programming model for clustering relational networks (2018)
  8. Álvarez-Miranda, Eduardo; Sinnl, Markus: A relax-and-cut framework for large-scale maximum weight connected subgraph problems (2017)
  9. Arcagni, Alberto; Grassi, Rosanna; Stefani, Silvana; Torriero, Anna: Higher order assortativity in complex networks (2017)
  10. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  11. Benjamin R. Fitzpatrick, Kerrie Mengersen: A network flow approach to visualising the roles of covariates in random forests (2017) arXiv
  12. 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
  13. Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
  14. Cinelli, Matteo; Ferraro, Giovanna; Iovanella, Antonio: Resilience of core-periphery networks in the case of rich-club (2017)
  15. Das, Bikramjit; Resnick, Sidney I.: Hidden regular variation under full and strong asymptotic dependence (2017)
  16. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  17. 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
  18. D. Ranathunga, H. Nguyen, M. Roughan: MGtoolkit: A python package for implementing metagraphs (2017)
  19. Gross, Elizabeth; Petrović, Sonja; Stasi, Despina: Goodness of fit for log-linear network models: dynamic Markov bases using hypergraphs (2017)
  20. Gunturi, Venkata M. V.; Shekhar, Shashi; Joseph, Kenneth; Carley, Kathleen M.: Scalable computational techniques for centrality metrics on temporally detailed social network (2017)

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