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Cython implementation of the classic Louvain algorithm for community detection in graphs

Project description

cylouvain is a Python module that provides a fast implementation of the classic Louvain algorithm for node clustering in graph.

This module uses Cython in order to obtain C-like performance with code mostly writen in Python.


Install the latest version of cylouvain using pip

$ pip install cylouvain


cylouvain requires:

  • Python (>= 2.7 or >= 3.4)

  • NumPy

  • SciPy

  • NetworkX

Simple example

Build a simple graph with NetworkX:

>>> import networkx as nx
>>> graph = nx.Graph()
>>> graph.add_nodes_from(['a', 'b', 'c', 'd', 'e'])
>>> graph.add_edges_from([('a', 'b'), ('a', 'c'), ('b', 'c'),
                          ('c', 'd'), ('c', 'e'), ('d', 'e')])

Compute a partition of the nodes using cylouvain:

>>> import cylouvain
>>> partition = cylouvain.best_partition(graph)
>>> print(partition)
{'a': 0, 'b': 0, 'c': 0, 'd': 1, 'e': 1}

Compute the corresponding modularity:

>>> modularity = cylouvain.modularity(partition, graph)
>>> print("Modularity: %0.3f\n" % modularity)
Modularity: 0.111


The Louvain algorithm is an heuristic to find a node partition that maximizes the modularity function. It is described in:

Fast unfolding of communities in large networks
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre
Journal of Statistical Mechanics: Theory and Experiment 2008 (10), P10008 (12pp)

The modularity function was first introduced in:

Finding and evaluating community structure in networks
Newman, Mark EJ and Girvan, Michelle
Physical review E, 2004, vol. 69, no 2, p. 026113.


Released under the 3-Clause BSD license (see COPYING):

Copyright (C) 2018 Alexandre Hollocou <>

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