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 134 articles )

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  1. J. Antonio Rivero Ostoic: Algebraic Analysis of Multiple Social Networks with multiplex (2020) not zbMATH
  2. Klusowski, Jason M.; Wu, Yihong: Estimating the number of connected components in a graph via subgraph sampling (2020)
  3. Li, Yang; Qi, Yongcheng: Asymptotic distribution of modularity in networks (2020)
  4. Modesto Escobar, Luis Martinez-Uribe: Network Coincidence Analysis: The netCoin R Package (2020) not zbMATH
  5. Serra, Paulo; Mandjes, Michel: Estimation of local degree distributions via local weighted averaging and Monte Carlo cross-validation (2020)
  6. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  7. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  8. Bien, Jacob: Graph-guided banding of the covariance matrix (2019)
  9. B. Perret; G. Chierchia; J. Cousty; S. J. F. Guimaraes; Y. Kenmochi; L. Najman: Higra: Hierarchical Graph Analysis (2019) not zbMATH
  10. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  11. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  12. Ding, Dewu: Network analysis of common differential genes identifies key genes and important modules underlying extracellular electron transfer processes (2019)
  13. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
  14. He, Kevin; Kang, Jian; Hong, Hyokyoung G.; Zhu, Ji; Li, Yanming; Lin, Huazhen; Xu, Han; Li, Yi: Covariance-insured screening (2019)
  15. Johnson, Brad C.; Kirkland, Steve: Estimating random walk centrality in networks (2019)
  16. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  17. Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham: ggenealogy: An R Package for Visualizing Genealogical Data (2019) not zbMATH
  18. Lozano, Manuel; Trujillo, Humberto M.: Optimizing node infiltrations in complex networks by a local search based heuristic (2019)
  19. Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
  20. Matsypura, Dmytro; Veremyev, Alexander; Prokopyev, Oleg A.; Pasiliao, Eduardo L.: On exact solution approaches for the longest induced path problem (2019)

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