yunhantu绘制学习

The library is based on the general definition of multilayer networks presented in a review article. Multilayer networks can be used to represent various types network generalizations found in the literature. For example, multiplex networks, temporal networks, networks of networks, and interdependent networks are all types of multilayer networks. The library supports even more general types of networks with multiple aspects such that the networks can for example have both temporal and multiplex aspect at the same time.

The visualization on the left is produced with the library. See the visualization tutorial for instructions on how to visualize your own network data with the library!

该库基于一篇综述文章中给出的多层网络的一般定义。多层网络可用于表示文献中发现的各种类型的网络概括。例如，多路网络、时间网络、网络的网络和相互依赖的网络都是多层网络的类型。该库甚至支持具有多个方面的更一般类型的网络，使得网络可以例如同时具有时间和复用方面。 左边的可视化是由库产生的。有关如何使用库可视化您自己的网络数据的说明，请参见可视化教程！

Main features include:

• Pure Python implementation

• Can handle general multilayer networks

• Data structures for multilayer networks and multiplex networks

• Scalable implementation for sparse networks: memory usage scales linearly with the number of edges and number of nodes

• Rule-based generation and lazy evaluation of coupling edges

• Various network analysis methods, transformations, reading and writing networks, network models, etc.

• Visualization (using matplotlib or D3 as a backend)

• Integration with NetworkX for monoplex network analysis