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 )

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  1. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  2. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  3. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  4. He, Kevin; Kang, Jian; Hong, Hyokyoung G.; Zhu, Ji; Li, Yanming; Lin, Huazhen; Xu, Han; Li, Yi: Covariance-insured screening (2019)
  5. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  6. Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham: ggenealogy: An R Package for Visualizing Genealogical Data (2019) not zbMATH
  7. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  8. Rui Portocarrero Sarmento, Luís Lemos, Mário Cordeiro, Giulio Rossetti, Douglas Cardoso: DynComm R Package - Dynamic Community Detection for Evolving Networks (2019) arXiv
  9. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  10. Ansmann, Gerrit: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE (2018)
  11. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  12. Boyd, Zachary M.; Bae, Egil; Tai, Xue-Cheng; Bertozzi, Andrea L.: Simplified energy landscape for modularity using total variation (2018)
  13. Devijver, Emilie; Gallopin, Mélina: Block-diagonal covariance selection for high-dimensional Gaussian graphical models (2018)
  14. Embrechts, P.; Kirchner, M.: Hawkes graphs (2018)
  15. Fairbrother, Jamie; Letchford, Adam N.; Briggs, Keith: A two-level graph partitioning problem arising in mobile wireless communications (2018)
  16. Goerigk, Marc; Hamacher, Horst W.; Kinscherff, Anika: Ranking robustness and its application to evacuation planning (2018)
  17. Griffin, Maryclare; Gile, Krista J.; Fredricksen-Goldsen, Karen I.; Handcock, Mark S.; Erosheva, Elena A.: A simulation-based framework for assessing the feasibility of respondent-driven sampling for estimating characteristics in populations of lesbian, gay and bisexual older adults (2018)
  18. Jin Zhu, Wenliang Pan, Wei Zheng, Xueqin Wang: Ball: An R package for detecting distribution difference and association in metric spaces (2018) arXiv
  19. Mair, Patrick: Modern psychometrics with R (2018)
  20. Martynov, Nikita Nikolaevich; Khandarova, Ol’ga Vladimirovna; Khandarov, Fëdor Vladimirovich: Graph clustering based on modularity variation estimations (2018)

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