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 142 articles )

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  1. Bazzan, Ana L. C.: I will be there for you: clique, character centrality, and community detection in \textitFriends (2020)
  2. Diem, Christian; Pichler, Anton; Thurner, Stefan: What is the minimal systemic risk in financial exposure networks? (2020)
  3. Fernando S. Marques, José H. H. Grisi-Filho, Jean C. R. Silva, Erivânia C. Almeida, José L. Silva Júnior: hybridModels: An R Package for the Stochastic Simulation of Disease Spreading in Dynamic Networks (2020) not zbMATH
  4. Fontanella, Lara; Fontanella, Sara; Ignaccolo, Rosaria; Ippoliti, Luigi; Valentini, Pasquale: G-Lasso network analysis for functional data (2020)
  5. Ikica, Barbara: Clustering via the modified Petford-Welsh algorithm (2020)
  6. J. Antonio Rivero Ostoic: Algebraic Analysis of Multiple Social Networks with multiplex (2020) not zbMATH
  7. Klusowski, Jason M.; Wu, Yihong: Estimating the number of connected components in a graph via subgraph sampling (2020)
  8. Li, Yang; Qi, Yongcheng: Asymptotic distribution of modularity in networks (2020)
  9. McGillivray, Annaliza; Khalili, Abbas; Stephens, David A.: Estimating sparse networks with hubs (2020)
  10. Modesto Escobar, Luis Martinez-Uribe: Network Coincidence Analysis: The netCoin R Package (2020) not zbMATH
  11. Serra, Paulo; Mandjes, Michel: Estimation of local degree distributions via local weighted averaging and Monte Carlo cross-validation (2020)
  12. S. Thomas Kelly; Michael A. Black: graphsim: An R package for simulating gene expression data from graph structures of biological pathways (2020) not zbMATH
  13. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  14. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  15. Bien, Jacob: Graph-guided banding of the covariance matrix (2019)
  16. B. Perret; G. Chierchia; J. Cousty; S. J. F. Guimaraes; Y. Kenmochi; L. Najman: Higra: Hierarchical Graph Analysis (2019) not zbMATH
  17. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  18. Dehmer, Matthias; Chen, Zengqiang; Emmert-Streib, Frank; Mowshowitz, Abbe; Shi, Yongtang; Tripathi, Shailesh; Zhang, Yusen: Towards detecting structural branching and cyclicity in graphs: a polynomial-based approach (2019)
  19. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  20. Ding, Dewu: Network analysis of common differential genes identifies key genes and important modules underlying extracellular electron transfer processes (2019)

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