If you have a cool new idea for a better method, and you want to optimise it, you can easily plug it in the current tool. This section explains how the package is setup internally, and how you can extend it. Most of this concerns C++, and python only comes in when exposing the resulting classes.


All methods in the end derive from MutableVertexPartition, which implements almost all necessary details, such as moving actual nodes while maintaining the internal administration. Similarly, it provides all the necessary functionality for initialising a partition. Additionally, there are two abstract classes that derive from this base class: ResolutionParameterVertexPartition and LinearResolutionParameterVertexPartition (which in turn derives from the former class). If you want a method with a resolution parameter, you should derive from one of these two classes, otherwise, simply from the base class MutableVertexPartition.

There are two functions that you need to implement yourself: diff_move() and quality(). Note that they should always be consistent, so that we can double check the internal consistency. You should also ensure that the diff_move function can be correctly used on any aggregate graph (i.e. moving a node in the aggregate graph indeed corresponds to moving a set of nodes in the individual graph).

That’s it. In principle, you could now use and test the method in C++.


Exposing the method to python takes a bit more effort. There are various places in which you need to change/add things. In the following, we assume you created a new class called CoolVertexPartition. In order of dependencies, it goes as follows:

  1. Your own new VertexPartition class should add some specific methods. In particular, you need to ensure you create a method

    CoolVertexPartition* CoolVertexPartition::create(Graph* graph)
      return new CoolVertexPartition(graph);


    CoolVertexPartition* CoolVertexPartition::create(Graph* graph, vector<size_t> const& membership)
      return new CoolVertexPartition(graph, membership);

    These methods ensure that based on a current partition, we can create a new partition (without knowing its type).

  2. In python_partition_interface.cpp some methods need to be added. In particular

    PyObject* _new_CoolVertexPartition(PyObject *self, PyObject *args, PyObject *keywds)

    You should be able to simply copy an existing method, and adapt it to your own needs.

  3. These methods need to be exposed in pynterface.h. In particular, you need to add the method you created in step (2) to louvain_funcs[]. Again, you should be able to simply copy an existing line.

  4. You can then finally create the Python class in The base class derives from the VertexClustering from igraph, so that it is compatible with all operations in igraph. You should add the method as follows:

    class CoolVertexPartition(MutableVertexPartition):
      def __init__(self, ... ):

    Again, you should be able to copy the outline for another class and adapt it to your own needs. Don’t forget to change to docstring to update the documentation so that everybody knows how your new cool method works.

  5. Expose your newly created python class directly in by importing it:

    from .VertexPartition import CoolVertexPartition

That’s it! You’re done and should now be able to find communities using your new CoolVertexPartition:

>>> louvain.find_partition(G, louvain.CoolVertexPartition);