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 124 articles )

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  1. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  2. Bien, Jacob: Graph-guided banding of the covariance matrix (2019)
  3. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  4. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  5. Ding, Dewu: Network analysis of common differential genes identifies key genes and important modules underlying extracellular electron transfer processes (2019)
  6. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
  7. He, Kevin; Kang, Jian; Hong, Hyokyoung G.; Zhu, Ji; Li, Yanming; Lin, Huazhen; Xu, Han; Li, Yi: Covariance-insured screening (2019)
  8. Johnson, Brad C.; Kirkland, Steve: Estimating random walk centrality in networks (2019)
  9. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  10. Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham: ggenealogy: An R Package for Visualizing Genealogical Data (2019) not zbMATH
  11. Lozano, Manuel; Trujillo, Humberto M.: Optimizing node infiltrations in complex networks by a local search based heuristic (2019)
  12. Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
  13. Matsypura, Dmytro; Veremyev, Alexander; Prokopyev, Oleg A.; Pasiliao, Eduardo L.: On exact solution approaches for the longest induced path problem (2019)
  14. Melina Vidoni; Aldo Vecchietti : rsppfp: An R package for the shortest path problem with forbidden paths (2019) not zbMATH
  15. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  16. Polzehl, Jörg; Tabelow, Karsten: Magnetic resonance brain imaging. Modeling and data analysis using R (2019)
  17. Rui Portocarrero Sarmento, Luís Lemos, Mário Cordeiro, Giulio Rossetti, Douglas Cardoso: DynComm R Package - Dynamic Community Detection for Evolving Networks (2019) arXiv
  18. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  19. Tanınmış, Kübra; Aras, Necati; Altınel, I. K.: Influence maximization with deactivation in social networks (2019)
  20. Yezerska, Oleksandra; Pajouh, Foad Mahdavi; Veremyev, Alexander; Butenko, Sergiy: Exact algorithms for the minimum (s)-club partitioning problem (2019)

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