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

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References in zbMATH (referenced in 84 articles )

Showing results 1 to 20 of 84.
Sorted by year (citations)

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  1. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  2. 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
  3. Kim, Jongeun; Veremyev, Alexander; Boginski, Vladimir; Prokopyev, Oleg A.: On the maximum small-world subgraph problem (2020)
  4. Chung, Jaewon; Pedigo, Benjamin D.; Bridgeford, Eric W.; Varjavand, Bijan K.; Helm, Hayden S.; Vogelstein, Joshua T.: GraSPY: graph statistics in Python (2019)
  5. Climaco, Joyce S.; Saa, Alberto: Optimal global synchronization of partially forced Kuramoto oscillators (2019)
  6. Consolini, Luca; Laurini, Mattia; Locatelli, Marco: Graph-based algorithms for the efficient solution of optimization problems involving monotone functions (2019)
  7. De Luca, Felice; Di Giacomo, Emilio; Didimo, Walter; Kobourov, Stephen; Liotta, Giuseppe: An experimental study on the ply number of straight-line drawings (2019)
  8. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  9. Gärtner, Fabian; Stadler, Peter F.: Direct superbubble detection (2019)
  10. 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)
  11. Haluszczynski, Alexander; Räth, Christoph: Good and bad predictions: assessing and improving the replication of chaotic attractors by means of reservoir computing (2019)
  12. Justel, Claudia; Rocha, Carlos; Chaves, Emanuelle; Chaves, Anderson; Avelino, Geraldo: Augmenting the algebraic connectivity for certain families of graphs (2019)
  13. Kamiyama, Naoyuki: Discrete Newton methods for the evacuation problem (2019)
  14. K. Hunter Wapman; Daniel B. Larremore: webweb: a tool for creating, displaying, and sharinginteractive network visualizations on the web (2019) not zbMATH
  15. Krishnan, Abin; Sujith, R. I.; Marwan, Norbert; Kurths, Jürgen: On the emergence of large clusters of acoustic power sources at the onset of thermoacoustic instability in a turbulent combustor (2019)
  16. Lozano, Manuel; Trujillo, Humberto M.: Optimizing node infiltrations in complex networks by a local search based heuristic (2019)
  17. Martin Fleischmann: momepy: Urban Morphology Measuring Toolkit (2019) not zbMATH
  18. Matsypura, Dmytro; Veremyev, Alexander; Prokopyev, Oleg A.; Pasiliao, Eduardo L.: On exact solution approaches for the longest induced path problem (2019)
  19. Michael E.Rose; John R.Kitchin: pybliometrics: Scriptable bibliometrics using a Python interface to Scopus (2019) not zbMATH
  20. Mortveit, Henning S.; Pederson, Ryan D.: Attractor stability in finite asynchronous biological system models (2019)

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