Exploring Network Structure, Dynamics, and Function Using NetworkX
Open accessAbstract
NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for easy exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdos-Renyi, Small World, and Barabasi-Albert models. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.
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POI VizNet centrality measures are based on NetworkX, a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
“POI VizNet centrality measures are based on NetworkXfoot_4 , a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks (Hagberg et al., 2008).”
From POI VizNet: New QGIS Tool to construct Visibility Networks in Cities (2024) · cites this paper as Hagberg et al., 2008
A comparison between various decomposition techniques has demonstrated the superiority of qbsolv in terms of solution quality.
“Acomparison between various decomposition techniques, i.e., principal component decomposition [185], free-and-anneal [163], qbSolv, and iterative centrality halo [172] has demonstrated the superiority of qbsolv in terms of solution quality [12].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [185]
The paper’s “NetworkX in action: synchronization” section compares algorithms for rewiring networks to improve synchronization, not decomposition techniques. Searches for qbSolv and the named decomposition methods found no passage assessing their solution quality. (LLM, conf=0.78)
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Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
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Research works printed but not linked: 7
- Eli Ben-Naim, Hans Frauenfelder, and Zoltan Torozckai, editors. Complex Networks, vol- ume 650 of Lecture Notes in Physics. Springer, 2004.
- J. Ellson, E.R. Gansner, E. Koutsofios, S.C. North, and G. Woodhull. Graphviz and dy- nagraph -static and dynamic graph draw- ing tools. In M. Junger and P. Mutzel, edi- tors, Graph Drawing Software, pages 127-148. Springer-Verlag, 2004.
- Kenneth S. Fink, Gregg Johnson, Tom Car- roll, Doug Mar, and Lou Pecora. Three cou- pled oscillators as a universal probe of synchro- nization stability in coupled oscillator arrays. Phys. Rev. E, 61(5):5080 -90, MAY 2000.
- Christopher R. Myers and James P. Sethna. Python for education: Computational meth- ods for nonlinear systems. Computing in Sci- ence and Engineering, 9(3):75-79, 2007.
- M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45(2):167 -256, June 2003.
- Robert Sedgewick. Algorithms in C: Part 5: Graph algorithms. Addison-Wesley, Reading, MA, USA, third edition, 2002.
- Todd L. Veldhuizen. Dynamic multilevel graph visualization. Eprint arXiv:cs.GR/07121549, Dec 2007.
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Dedieu, A., Lehrach, W., Zhou, G., George, D., & Lázaro-Gredilla, M. (2024) · arXiv · doi:10.48550/arxiv.2401.05946 · Cited by 2 · see citing works
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Barmettler, J., Bernstein, A., & Rossetto, L. (2025) · arXiv · doi:10.48550/arxiv.2504.07624
“The JSON structure is easily loaded into tools such as NetworkX [15], enabling further graph-based processing or embedding.”
Chan, V. S., Mohamed, F. B., Salleh, F. B. M., Iglesias, A., Abdul-Rahman, A., Miga, M., Najjar, F. H., & Cao, C. (2026) · Information Visualization · doi:10.1177/14738716261498353
- Authors
- Hagberg, A. A., Schult, D. A., & Swart, P. J.
- Affiliations
- Los Alamos National Laboratory (US)
- Colgate University, Hamilton, NY USA
- ORCID
- Hagberg, Aric A.
- Venue
- Proceedings of the Python in Science Conference
- Publisher
- SciPy
- Published
- 2008-06-28
- Pages
- 11-15
- ISSN
- 2575-9752 (ISSN-L)
- OpenAlex
- W2132022337
- Cited by
- 7,477
- References
- 1
- Citations by year
- 2026: 830 · 2025: 1016 · 2024: 893 · 2023: 805 · 2022: 714 · 2021: 836 · 2020: 564 · 2019: 455 · 2018: 309 · 2017: 231 · 2016: 221 · 2015: 182 · 2014: 152 · 2013: 104 · 2012: 90
- FWCI
- 29.24
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the Python in Science Conferences (publishedVersion, cc-by)
- Funders
- U.S. Department of Energy (DE-AC52-06NA2-5396, AC52-06NA25396)
- National Nuclear Security Administration (DEAC52- 06NA25396)
- Los Alamos National Laboratory (AC52-06NA25396, DE-AC52-06NA25396)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Computational Physics and Python Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Keywords
- NetworkX, network analysis, graph data structures, graph algorithms, network characteristics, graph generators, Erdős-Rényi random graphs, small-world networks, Barabási-Albert model, computational networks
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