NetworkX

A graph created with NetworkX

Original author(s)| Aric Hagberg
Pieter Swart
Dan Schult
Developer(s)| Many others
Initial release| 11 April 2005; 18 years ago (2005-04-11)[1][2]
Written in| Python
Operating system| Cross-platform
Type| Software library
License| BSD-new license
Website| networkx.github.io
Network science

* Theory

* Graph
* Complex network
* Contagion
* Small-world
* Scale-free
* Community structure
* Percolation
* Evolution
* Controllability
* Graph drawing
* Social capital
* Link analysis
* Optimization
* Reciprocity
* Closure
* Homophily
* Transitivity
* Preferential attachment
* Balance theory
* Network effect
* Social influence

Network types

* Informational (computing)
* Telecommunication
* Transport
* Social
* Scientific collaboration
* Biological
* Artificial neural
* Interdependent
* Semantic
* Spatial
* Dependency
* Flow
* on-Chip

Graphs
|  Features

* Clique
* Component
* Cut
* Cycle
* Data structure
* Edge
* Loop
* Neighborhood
* Path
* Vertex
* Adjacency list / matrix
* Incidence list / matrix

Types

* Bipartite
* Complete
* Directed
* Hyper
* Multi
* Random
* Weighted

* Metrics
* Algorithms

* Centrality
* Degree
* Betweenness
* Closeness
* PageRank
* Motif
* Clustering
* Degree distribution
* Assortativity
* Distance
* Modularity
* Efficiency

Models
|  Topology

* Random graph
* Erdős–Rényi
* Barabási–Albert
* Fitness model
* Watts–Strogatz
* Exponential random (ERGM)
* Random geometric (RGG)
* Hyperbolic (HGN)
* Hierarchical
* Stochastic block
* Maximum entropy
* Soft configuration
* LFR Benchmark

Dynamics

* Boolean network
* agent based
* Epidemic/SIR

* Lists
* Categories

* Topics
* Software
* Network scientists

* Category:Network theory
* Category:Graph theory

* v
* t
* e

NetworkX is a Python library for studying graphs and networks. NetworkX is free software released under the BSD-new license.

## Contents

* 1 Features
* 2 Suitability
* 3 Integration
* 4 See also
* 5 References
* 6 External links

## Features

* Classes for graphs and digraphs.
* Conversion of graphs to and from several formats.
* Ability to construct random graphs or construct them incrementally.
* Ability to find subgraphs, cliques, k-cores.
* Explore adjacency, degree, diameter, radius, center, betweenness, etc.
* Draw networks in 2D and 3D.

## Suitability

NetworkX is suitable for operation on large real-world graphs: e.g., graphs in excess of 10 million nodes and 100 million edges.[clarification needed][3] Due to its dependence on a pure-Python "dictionary of dictionary" data structure, NetworkX is a reasonably efficient, very scalable, highly portable framework for network and social network analysis.[4]

## Integration

NetworkX is integrated into SageMath.[5]

## See also

* Social network analysis software
* JGraph

## References

1. ↑ NetworkX first public release (NX-0.2), From: Aric Hagberg, Date: 12 April 2005, Python-announce-list mailing list
2. ↑ NetworkX initial release, NX-0.2, hagberg – 2005-04-11, Project Info – NetworkX, Registered: 2004-10-21, SourceForge.net
3. ↑ Aric Hagberg, Drew Conway, "Hacking social networks using the Python programming language (Module II – Why do SNA in NetworkX)", Sunbelt 2010: International Network for Social Network Analysis.
4. ↑ Aric A. Hagberg, Daniel A. Schult, Pieter J. Swart, Exploring Network Structure, Dynamics, and Function using NetworkX, Proceedings of the 7th Python in Science conference (SciPy 2008), G. Varoquaux, T. Vaught, J. Millman (Eds.), pp. 11–15.
5. ↑ "SageMath Mathematical Software System - Sage". http://sagemath.org/links-components.html.

## External links

* Official website: networkx.github.io
* NetworkX discussion group
* Survey of existing graph theory software
* NetworkX on StackOverflow
* on GitHub

* v
* t
* e

Graph analysis software

Free|

* Cytoscape
* Graphviz
* Gephi
* iGraph
* NetworkX
* Tulip

Proprietary|

* Maple
* Mathematica

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