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This package has been superseded by the leidenalg package and will no longer be maintained.


This package implements the louvain algorithm in C++ and exposes it to python. It relies on (python-)igraph for it to function. Besides the relative flexibility of the implementation, it also scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The core function is find_partition which finds the optimal partition using the louvain algorithm1 for a number of different methods. The methods currently implemented are (1) modularity2, (2) Reichardt and Bornholdt's model using the configuration null model and the Erdös-Rényi null model3, (3) the constant Potts model (CPM)4, (4) Significance5, and finally (5) Surprise6. In addition, it supports multiplex partition optimisation allowing community detection on for example negative links7 or multiple time slices8. It also provides some support for community detection on bipartite graphs. See the documentation for more information.

Louvain documentation status

Louvain build status


Anaconda (conda-forge)


In short: pip install louvain. All major platforms are supported on Python>=3.5, earlier versions of Python are no longer supported. Alternatively, you can install from Anaconda (channels conda-forge).

For Unix like systems it is possible to install from source. For Windows this is overly complicated, and you are recommended to use the binary wheels. The igraph C core library is provided within this package, and is automatically compiled. If you encounter any issue with compilation, please see

Make sure you have all necessary tools for compilation. In Ubuntu this can be installed using sudo apt-get install build-essential autoconf automake flex bison, please refer to the documentation for your specific system. Make sure that not only gcc is installed, but also g++, as the louvain-igraph package is programmed in C++.

You can check if all went well by running a variety of tests using python test.

There are basically two installation modes, similar to the python-igraph package itself (from which most of the comes).

  1. No C core library is installed yet. The C core library of igraph that is provided within the louvain-igraph package is compiled.
  2. A C core library is already installed. In this case, you may link dynamically to the already installed version by specifying --no-pkg-config. This is probably also the version that is used by the igraph package, but you may want to double check this.

In case the python-igraph package is already installed before, make sure that both use the same versions (at least the same minor version, which should be API compatible).


In case of any problems, best to start over with a clean environment. Make sure you remove the python-igraph package completely, remove the C core library and remove the louvain-igraph package. Then, do a complete reinstall starting from pip install louvain-igraph. In case you want a dynamic library be sure to then install the C core library from source before. Make sure you install the same versions.


There is no standalone version of louvain-igraph, and you will always need python to access it. There are no plans for developing a standalone version or R support. So, use python. Please refer to the documentation for more details on function calls and parameters.

Just to get you started, below the essential parts. To start, make sure to import the packages:

>>> import louvain >>> import igraph as ig

We'll create a random graph for testing purposes:

>>> G = ig.Graph.Erdos_Renyi(100, 0.1);

For simply finding a partition use:

>>> part = louvain.find_partition(G, louvain.ModularityVertexPartition);


Source code:

Issue tracking:

See the documentation on Implementation for more details on how to contribute new methods.


Please cite the references appropriately in case they are used.


Copyright (C) 2020 V.A. Traag

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see

  1. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10008(10), 6. 10.1088/1742-5468/2008/10/P10008

  2. Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. 10.1103/PhysRevE.69.026113

  3. Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74(1), 016110. 10.1103/PhysRevE.74.016110

  4. Traag, V. A., Van Dooren, P., & Nesterov, Y. (2011). Narrow scope for resolution-limit-free community detection. Physical Review E, 84(1), 016114. 10.1103/PhysRevE.84.016114

  5. Traag, V. A., Krings, G., & Van Dooren, P. (2013). Significant scales in community structure. Scientific Reports, 3, 2930. 10.1038/srep02930

  6. Traag, V. A., Aldecoa, R., & Delvenne, J.-C. (2015). Detecting communities using asymptotical surprise. Physical Review E, 92(2), 022816. 10.1103/PhysRevE.92.022816

  7. Traag, V. A., & Bruggeman, J. (2009). Community detection in networks with positive and negative links. Physical Review E, 80(3), 036115. 10.1103/PhysRevE.80.036115

  8. Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–8. 10.1126/science.1184819


Implementation of the Louvain algorithm for community detection with various methods for use with igraph in python.








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