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 27 articles )

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  1. Bar-Hen, Avner; Poggi, Jean-Michel: Influence measures and stability for graphical models (2016)
  2. 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)
  3. 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)
  4. Nunes, Davide; Antunes, Luis: Modelling structured societies: a multi-relational approach to context permeability (2015)
  5. Rossi, Ryan A.; Gleich, David F.; Gebremedhin, Assefaw H.: Parallel maximum clique algorithms with applications to network analysis (2015)
  6. 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)
  7. Duncan, Melissa; Gu, Wei; He, Yang-Hui; Zhou, Da: The statistics of vacuum geometry (2014)
  8. Fontana, Roberto: Random Latin squares and Sudoku designs generation (2014)
  9. Kolaczyk, Eric D.; Csárdi, Gábor: Statistical analysis of network data with R (2014)
  10. Leger, Jean-Benoist; Vacher, Corinne; Daudin, Jean-Jacques: Detection of structurally homogeneous subsets in graphs (2014)
  11. Manitz, Juliane: Statistical inference for propagation processes on complex networks (2014)
  12. Ma, Shuai; Cao, Yang; Fan, Wenfei; Huai, Jinpeng; Wo, Tianyu: Strong simulation: capturing topology in graph pattern matching (2014)
  13. Michalski, Radosław; Kajdanowicz, Tomasz; Bródka, Piotr; Kazienko, Przemysław: Seed selection for spread of influence in social networks: temporal vs. static approach (2014)
  14. Antulov-Fantulin, Nino; Lančić, Alen; Štefančić, Hrvoje; Šikić, Mile: FastSIR algorithm: a fast algorithm for the simulation of the epidemic spread in large networks by using the susceptible-infected-recovered compartment model (2013)
  15. Holst, Klaus Kähler; Budtz-Jørgensen, Esben: Linear latent variable models: the lava-package (2013)
  16. Moewes, Christian; Kruse, Rudolf; Sabel, Bernhard A.: Analysis of dynamic brain networks using VAR models (2013)
  17. Nettleton, David F.: Data mining of social networks represented as graphs (2013)
  18. Peterson, Christine; Vannucci, Marina; Karakas, Cemal; Choi, William; Ma, Lihua; Maletic-Savatic, Mirjana: Inferring metabolic networks using the Bayesian adaptive graphical Lasso with informative priors (2013)
  19. Riesen, Kaspar; Emmenegger, Sandro; Bunke, Horst: A novel software toolkit for graph edit distance computation (2013)
  20. Zhao, Yanchang: R and data mining. Examples and case studies (2013)

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