# Software for Complex Networks¶

- Release
2.7rc1.dev0

- Date
Nov 25, 2021

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It provides:

tools for the study of the structure and dynamics of social, biological, and infrastructure networks;

a standard programming interface and graph implementation that is suitable for many applications;

a rapid development environment for collaborative, multidisciplinary projects;

an interface to existing numerical algorithms and code written in C, C++, and FORTRAN; and

the ability to painlessly work with large nonstandard data sets.

With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more.

## Citing¶

To cite NetworkX please use the following publication:

Aric A. Hagberg, Daniel A. Schult and Pieter J. Swart, “Exploring network structure, dynamics, and function using NetworkX”, in Proceedings of the 7th Python in Science Conference (SciPy2008), Gäel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pp. 11–15, Aug 2008

## Audience¶

The audience for NetworkX includes mathematicians, physicists, biologists, computer scientists, and social scientists. Good reviews of the science of complex networks are presented in Albert and Barabási [BA02], Newman [Newman03], and Dorogovtsev and Mendes [DM03]. See also the classic texts [Bollobas01], [Diestel97] and [West01] for graph theoretic results and terminology. For basic graph algorithms, we recommend the texts of Sedgewick (e.g., [Sedgewick01] and [Sedgewick02]) and the survey of Brandes and Erlebach [BE05].

## Python¶

Python is a powerful programming language that allows simple and flexible representations of networks as well as clear and concise expressions of network algorithms. Python has a vibrant and growing ecosystem of packages that NetworkX uses to provide more features such as numerical linear algebra and drawing. In order to make the most out of NetworkX you will want to know how to write basic programs in Python. Among the many guides to Python, we recommend the Python documentation and the text by Alex Martelli [Martelli03].

## License¶

NetworkX is distributed with the 3-clause BSD license.

```
Copyright (C) 2004-2021, NetworkX Developers
Aric Hagberg <hagberg@lanl.gov>
Dan Schult <dschult@colgate.edu>
Pieter Swart <swart@lanl.gov>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of the NetworkX Developers nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
```

## Bibliography¶

- BA02
R. Albert and A.-L. Barabási, “Statistical mechanics of complex networks”, Reviews of Modern Physics, 74, pp. 47-97, 2002. https://arxiv.org/abs/cond-mat/0106096

- Bollobas01
B. Bollobás, “Random Graphs”, Second Edition, Cambridge University Press, 2001.

- BE05
U. Brandes and T. Erlebach, “Network Analysis: Methodological Foundations”, Lecture Notes in Computer Science, Volume 3418, Springer-Verlag, 2005.

- Diestel97
R. Diestel, “Graph Theory”, Springer-Verlag, 1997. http://diestel-graph-theory.com/index.html

- DM03
S.N. Dorogovtsev and J.F.F. Mendes, “Evolution of Networks”, Oxford University Press, 2003.

- Martelli03
A. Martelli, “Python in a Nutshell”, O’Reilly Media Inc, 2003.

- Newman03
M.E.J. Newman, “The Structure and Function of Complex Networks”, SIAM Review, 45, pp. 167-256, 2003. http://epubs.siam.org/doi/abs/10.1137/S003614450342480

- Sedgewick02
R. Sedgewick, “Algorithms in C: Parts 1-4: Fundamentals, Data Structure, Sorting, Searching”, Addison Wesley Professional, 3rd ed., 2002.

- Sedgewick01
R. Sedgewick, “Algorithms in C, Part 5: Graph Algorithms”, Addison Wesley Professional, 3rd ed., 2001.

- West01
D. B. West, “Introduction to Graph Theory”, Prentice Hall, 2nd ed., 2001.