Compute diffusion scores over networks
Project description
Introduction
![Documentation Status](https://pypi-camo.freetls.fastly.net/22b395d125a997a8571eb7a1bd0c7ad0bfcff2b0/687474703a2f2f72656164746865646f63732e6f72672f70726f6a656374732f646966667570792f62616467652f3f76657273696f6e3d6c6174657374)
DiffuPy is a generalizable Python implementation of the numerous label propagation algorithms. DiffuPy supports generic graph formats such as JSON, CSV, GraphML, or GML. Check out DiffuPy’s documentation here.
Installation
![Apache-2.0](https://pypi-camo.freetls.fastly.net/fb89683330d924f06ec2172e57a543239e6b502f/68747470733a2f2f696d672e736869656c64732e696f2f707970692f6c2f646966667570792e737667)
The latest stable code can be installed from PyPI with:
$ python3 -m pip install diffupy
The most recent code can be installed from the source on GitHub with:
$ python3 -m pip install git+https://github.com/multipaths/DiffuPy.git
For developers, the repository can be cloned from GitHub and installed in editable mode with:
$ git clone https://github.com/multipaths/DiffuPy.git
$ cd diffupy
$ python3 -m pip install -e .
Command Line Interface
The following commands can be used directly from your terminal:
1. Run a diffusion analysis The following command will run a diffusion method on a given network with the given data. More information here.
$ python3 -m diffupy diffuse --network=<path-to-network-file> --data=<path-to-data-file> --method=<method>
2. Generate a kernel with one of the seven methods implemented Generates the regularised Laplacian kernel of a given graph. More information in the documentation.
$ python3 -m diffupy kernel --network=<path-to-network-file>
Disclaimer
DiffuPy is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or guarantee of maintenance, support, or back-up of data.
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