arnaudon/PyGenStability

Python wrapper of the generalised Louvain, including Markov Stability

PyGenStability

Python wrapper of the generalised Louvain code of Michael Schaub at https://github.com/michaelschaub/generalizedLouvain with python code to run various versions of Markov Stability.

Installation

The wrapper uses Pybind11 https://github.com/pybind/pybind11 and the package can simply be installed by first cloning this repo with

git clone --recurse-submodules https://github.com/ImperialCollegeLondon/PyGenStability.git

(if the --recurse-submodules has not been used, just do git submodule update --init --recursive to fetch the submodule with M. Schaub's code).

Then, to install the package, simply run

pip install . 

using a fresh virtualenv in python3 may be recommanded to avoid conflict of python packages.

To use plotly for interacting plos in browser, install this package with

pip install .[plotly]

To use contrib module, with additional tools, run

pip install .[contrib]

To install all dependencies, run

pip install .[all]

Documentation

Documentation is here: https://barahona-research-group.github.io/PyGenStability/

Example

In the example folder, a demo script with stochastic block model can be tried with

python simple_example.py

or using the click app:

./run_simple_example.sh

Custom constructors

The generalized Louvain code needs a constructor, which can be a function with the following properties:

• take a networkx graph and a float time as argument
• return a quality_matrix (sparse scipy matrix) and a null_model (multiples of two, in a numpy array)

Please see pygenstability/constructors.py for some classic examples.

Contrib

This module contains various additional tools one can use. Currently it contains:

• optimal-scales: to find and plot optimal scales accros time
• sankey: plot sankey diagrams of clusters accros time

Our other available packages

If you are interested in trying our other packages, see the below list:

• GDR : Graph diffusion reclassification. A methodology for node classification using graph semi-supervised learning.
• hcga : Highly comparative graph analysis. A graph analysis toolbox that performs massive feature extraction from a set of graphs, and applies supervised classification methods.
• MSC : MultiScale Centrality: A scale dependent metric of node centrality.
• DynGDim : Dynamic Graph Dimension: Computing the relative, local and global dimension of complex networks.
• PyGenStability : Markov Stability: Computing the Markov Stability graph community detection algorithm in Python.
• RMST : Relaxed Minimum Spanning Tree: Computing the relaxed minimum spanning tree to sparsify networks whilst retaining dynamic structure.
• StEP : Spatial-temporal Epidemiological Proximity: Characterising contact in disease outbreaks via a network model of spatial-temporal proximity.