NetworkX

NetworkX (NX) is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

This software is also referenced in ORMS.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 90 articles )

Showing results 1 to 20 of 90.
Sorted by year (citations)

1 2 3 4 5 next

  1. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  2. García Soto, Miriam; Prabhakar, Pavithra: Abstraction based verification of stability of polyhedral switched systems (2020)
  3. Joel C. Miller, Tony TIng: EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks (2020) arXiv
  4. Kim, Jongeun; Veremyev, Alexander; Boginski, Vladimir; Prokopyev, Oleg A.: On the maximum small-world subgraph problem (2020)
  5. Liberti, Leo: Distance geometry and data science (2020)
  6. Modesto Escobar, Luis Martinez-Uribe: Network Coincidence Analysis: The netCoin R Package (2020) not zbMATH
  7. Aloui, Omar; Flores, Jessica; Orden, David; Rhode-Barbarigos, Landolf: Cellular morphogenesis of three-dimensional tensegrity structures (2019)
  8. B. Perret; G. Chierchia; J. Cousty; S. J. F. Guimaraes; Y. Kenmochi; L. Najman: Higra: Hierarchical Graph Analysis (2019) not zbMATH
  9. Chung, Jaewon; Pedigo, Benjamin D.; Bridgeford, Eric W.; Varjavand, Bijan K.; Helm, Hayden S.; Vogelstein, Joshua T.: GraSPY: graph statistics in Python (2019)
  10. Climaco, Joyce S.; Saa, Alberto: Optimal global synchronization of partially forced Kuramoto oscillators (2019)
  11. Consolini, Luca; Laurini, Mattia; Locatelli, Marco: Graph-based algorithms for the efficient solution of optimization problems involving monotone functions (2019)
  12. De Luca, Felice; Di Giacomo, Emilio; Didimo, Walter; Kobourov, Stephen; Liotta, Giuseppe: An experimental study on the ply number of straight-line drawings (2019)
  13. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  14. Gärtner, Fabian; Stadler, Peter F.: Direct superbubble detection (2019)
  15. Godefroy, G.; Caumon, G.; Laurent, G.; Bonneau, F.: Structural interpretation of sparse fault data using graph theory and geological rules. Structural interpretation of sparse fault data using graph theory and geological rules, fault data interpretation (2019)
  16. Haluszczynski, Alexander; Räth, Christoph: Good and bad predictions: assessing and improving the replication of chaotic attractors by means of reservoir computing (2019)
  17. Joana M. F. da Trindade, Konstantinos Karanasos, Carlo Curino, Samuel Madden, Julian Shun: Kaskade: Graph Views for Efficient Graph Analytics (2019) arXiv
  18. Justel, Claudia; Rocha, Carlos; Chaves, Emanuelle; Chaves, Anderson; Avelino, Geraldo: Augmenting the algebraic connectivity for certain families of graphs (2019)
  19. Kamiyama, Naoyuki: Discrete Newton methods for the evacuation problem (2019)
  20. K. Hunter Wapman; Daniel B. Larremore: webweb: a tool for creating, displaying, and sharinginteractive network visualizations on the web (2019) not zbMATH

1 2 3 4 5 next