# pymnet: A Python Library for Multilayer Networks
[](https://codecov.io/gh/mnets/pymnet)
`pymnet` is a Python package for creating, analyzing, and visualizing multilayer networks as formalized by [Kivelä et al. (2014)](https://doi.org/10.1093/comnet/cnu016).
It is designed for network scientists with an easy-to-use yet flexible interface, featuring, inter alia, representations of a very general class of multilayer networks, structural metrics of multilayer networks, and random multilayer-network models.
To learn more about the concepts and design principles underlying `pymnet`, check out [this overview](https://mnets.github.io/pymnet/overview.html).
## Features
* Written in pure Python
* Full support for general [multilayer networks](http://comnet.oxfordjournals.org/content/2/3/203)
* Efficient handling of multiplex networks (with automatically generated lazy evaluation of coupling edges)
* Extensive functionality –– analysis, transformations, reading and writing networks, network models, etc.
* Flexible multilayer-network visualization (using Matplotlib and D3)
* Integration with [NetworkX](https://networkx.org/) for monoplex network analysis
## Working with pymnet
### Installation
We recommend executing the following command in a virtual environment:
```console
$ python -m pip install pymnet
```
### Usage
To get started with `pymnet`, check out our [tutorials](https://mnets.github.io/pymnet/tutorials) –– and when in doubt, consult the [API reference](https://mnets.github.io/pymnet/reference.html) contained in our [documentation](https://mnets.github.io/pymnet/).
As an introductory example, with the following code, we can create a small multiplex network capturing different types of social relations between individuals and visualize the result:
```python
import pymnet
net_social = pymnet.MultiplexNetwork(couplings="categorical", fullyInterconnected=False)
net_social["Alice", "Bob", "Friends"] = 1
net_social["Alice", "Carol", "Friends"] = 1
net_social["Bob", "Carol", "Friends"] = 1
net_social["Alice", "Bob", "Married"] = 1
fig_social = pymnet.draw(net_social, layout="circular", layerPadding=0.2, defaultLayerLabelLoc=(0.9,0.9))
```
<p align="center" style="margin-top:-6rem;margin-bottom:-3rem">
<img alt="An image of a small multiplex social network." width="60%" src="https://github.com/mnets/pymnet/raw/master/socialnet.png">
</p>
## Contributing
We welcome contributions!
Before you get started, please check out our [contribution guide](CONTRIBUTING.md).
## Asking Questions
* For bugs, feature requests, etc., please use [GitHub issues][github-issues].
* Otherwise, feel free to contact the main developer: [Mikko Kivelä](http://www.mkivela.com/)
[github-issues]: https://github.com/mnets/pymnet/issues
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"description": "# pymnet: A Python Library for Multilayer Networks\n\n[](https://codecov.io/gh/mnets/pymnet)\n\n`pymnet` is a Python package for creating, analyzing, and visualizing multilayer networks as formalized by [Kivel\u00e4 et al. (2014)](https://doi.org/10.1093/comnet/cnu016).\nIt is designed for network scientists with an easy-to-use yet flexible interface, featuring, inter alia, representations of a very general class of multilayer networks, structural metrics of multilayer networks, and random multilayer-network models. \n\nTo learn more about the concepts and design principles underlying `pymnet`, check out [this overview](https://mnets.github.io/pymnet/overview.html).\n\n## Features\n\n* Written in pure Python\n* Full support for general [multilayer networks](http://comnet.oxfordjournals.org/content/2/3/203)\n* Efficient handling of multiplex networks (with automatically generated lazy evaluation of coupling edges)\n* Extensive functionality \u2013\u2013 analysis, transformations, reading and writing networks, network models, etc.\n* Flexible multilayer-network visualization (using Matplotlib and D3)\n* Integration with [NetworkX](https://networkx.org/) for monoplex network analysis\n\n## Working with pymnet\n\n### Installation\nWe recommend executing the following command in a virtual environment: \n```console\n$ python -m pip install pymnet\n```\n\n### Usage\nTo get started with `pymnet`, check out our [tutorials](https://mnets.github.io/pymnet/tutorials) \u2013\u2013 and when in doubt, consult the [API reference](https://mnets.github.io/pymnet/reference.html) contained in our [documentation](https://mnets.github.io/pymnet/).\n\nAs an introductory example, with the following code, we can create a small multiplex network capturing different types of social relations between individuals and visualize the result:\n\n```python\nimport pymnet\n\nnet_social = pymnet.MultiplexNetwork(couplings=\"categorical\", fullyInterconnected=False)\nnet_social[\"Alice\", \"Bob\", \"Friends\"] = 1\nnet_social[\"Alice\", \"Carol\", \"Friends\"] = 1\nnet_social[\"Bob\", \"Carol\", \"Friends\"] = 1\nnet_social[\"Alice\", \"Bob\", \"Married\"] = 1\n\nfig_social = pymnet.draw(net_social, layout=\"circular\", layerPadding=0.2, defaultLayerLabelLoc=(0.9,0.9))\n```\n\n<p align=\"center\" style=\"margin-top:-6rem;margin-bottom:-3rem\">\n <img alt=\"An image of a small multiplex social network.\" width=\"60%\" src=\"https://github.com/mnets/pymnet/raw/master/socialnet.png\"> \n</p>\n\n\n## Contributing\n\nWe welcome contributions!\nBefore you get started, please check out our [contribution guide](CONTRIBUTING.md).\n\n## Asking Questions\n\n* For bugs, feature requests, etc., please use [GitHub issues][github-issues].\n* Otherwise, feel free to contact the main developer: [Mikko Kivel\u00e4](http://www.mkivela.com/)\n\n[github-issues]: https://github.com/mnets/pymnet/issues\n",
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