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Complex networks - common drawing software and libraries
2022-07-23 13:15:00 【Ling Xianwen】
Catalog
1、NetworkX
NetworkX It's a use. Python Graph theory and complex network modeling tools for language development , Built in common graph and complex network analysis algorithm , It is convenient for complex network data analysis 、 Simulation modeling and other work .networkx Supports the creation of simple undirected graphs 、 Digraphs and multigraphs ; Built in many standard graph theory algorithms , Nodes can be arbitrary data ; Supports any boundary value dimension , Rich in functions . Mainly used to create 、 Operating complex networks , And learning the structure of complex networks 、 Dynamics and its functions . Used to analyze the network structure
Build a network model , Design a new network algorithm , Drawing networks and so on .
Gallery — NetworkX 3.0b1.dev0 documentation


Very powerful , Can generate ER、SF、 Small world and other networks , Directed network, etc .
2、graphviz
Graphviz Is an open source graphics visualization software . Graph visualization is a method of representing structural information as abstract graphs and network graphs . It's on the Internet 、 Bioinformatics 、 Software Engineering 、 Database and web Design 、 Machine learning and other technical fields have important applications in visual interface .
Graphviz The layout program describes graphics in simple text language , And in a useful format ( Such as web The image of the page and SVG) Generate graphics ;PDF Or postscript , For inclusion in other documents ; Or display in an interactive graphics browser .Graphviz There are many useful features for specific charts , For example, color. 、 typeface 、 Table node layout 、 Row style 、 Options for hyperlinks and custom shapes .


3、gnuplot
A scientific mapping software



Very, very powerful 、 Be similar to MATLAB, But there are a lot of demo, And it's convenient .
4、graph-tool
Professional drawing . chart , The Internet .
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other Python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

5、matplotlib and seaborn
It's quite extensive , No more details .
But I use it most hahaha .

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