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 111 articles )

Showing results 61 to 80 of 111.
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  1. 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)
  2. Dahm, Nicholas; Bunke, Horst; Caelli, Terry; Gao, Yongsheng: Efficient subgraph matching using topological node feature constraints (2015)
  3. Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen: Unified functional network and nonlinear time series analysis for complex systems science: the pyunicorn package (2015)
  4. 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
  5. 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)
  6. Mohammadi, A.; Wit, E.C.: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models (2015) arXiv
  7. Nunes, Davide; Antunes, Luis: Modelling structured societies: a multi-relational approach to context permeability (2015)
  8. Rossi, Ryan A.; Gleich, David F.; Gebremedhin, Assefaw H.: Parallel maximum clique algorithms with applications to network analysis (2015)
  9. Dimeglio, Chloé; Gallón, Santiago; Loubes, Jean-Michel; Maza, Elie: A robust algorithm for template curve estimation based on manifold embedding (2014)
  10. Dørum, Guro; Snipen, Lars; Solheim, Margrete; Sæbø, Solve: Rotation gene set testing for longitudinal expression data (2014)
  11. 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)
  12. Duncan, Melissa; Gu, Wei; He, Yang-Hui; Zhou, Da: The statistics of vacuum geometry (2014)
  13. Fontana, Roberto: Random Latin squares and Sudoku designs generation (2014)
  14. Jan Thiele: R Marries NetLogo: Introduction to the RNetLogo Package (2014) not zbMATH
  15. Jay Ver Hoef; Erin Peterson; David Clifford; Rohan Shah: SSN: An R Package for Spatial Statistical Modeling on Stream Networks (2014) not zbMATH
  16. Kolaczyk, Eric D.; Csárdi, Gábor: Statistical analysis of network data with R (2014)
  17. Kraus, Veronika; Dehmer, Matthias; Emmert-Streib, Frank: Probabilistic inequalities for evaluating structural network measures (2014)
  18. Leger, Jean-Benoist; Vacher, Corinne; Daudin, Jean-Jacques: Detection of structurally homogeneous subsets in graphs (2014)
  19. Manitz, Juliane: Statistical inference for propagation processes on complex networks (2014)
  20. Ma, Shuai; Cao, Yang; Fan, Wenfei; Huai, Jinpeng; Wo, Tianyu: Strong simulation: capturing topology in graph pattern matching (2014)