Leiden is a general algorithm for methods of community detection in large networks.
This package implements the Leiden 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
Leiden algorithm [1], which is an extension of the Louvain algorithm [2] for a
number of different methods. The methods currently implemented are (1)
modularity [3], (2) Reichardt and Bornholdt's model using the configuration
null model and the Erdös-Rényi null model [4], (3) the Constant Potts model
(CPM) [5], (4) Significance [6], and finally (5) Surprise [7]. In addition,
it supports multiplex partition optimisation allowing community detection on for
example negative links [8] or multiple time slices [9]. There is the
possibility of only partially optimising a partition, so that some community
assignments remain fixed [10]. It also provides some support for community
detection on bipartite graphs. See the documentation <http://leidenalg.readthedocs.io/en/latest/>_ for more information.
.. image:: https://readthedocs.org/projects/leidenalg/badge :target: http://leidenalg.readthedocs.io/en/latest/ :alt: Leiden documentation status
.. image:: https://github.com/vtraag/leidenalg/actions/workflows/build.yml/badge.svg?branch=master :target: https://github.com/vtraag/leidenalg/actions/workflows/build.yml :alt: Leiden build status (GitHub Actions)
.. image:: https://zenodo.org/badge/146722095.svg :target: https://zenodo.org/badge/latestdoi/146722095 :alt: DOI
.. image:: https://anaconda.org/conda-forge/leidenalg/badges/version.svg :target: https://anaconda.org/conda-forge/leidenalg :alt: Anaconda (conda-forge)
In short: pip install leidenalg. All major platforms are supported on
Python>=3.6, earlier versions of Python are no longer supported. Alternatively,
you can install from Anaconda (channel conda-forge).
For Unix like systems it is possible to install from source. For Windows this is
more complicated, and you are recommended to use the binary wheels. This Python
interface depends on the C++ package libleidenalg which in turn depends on
igraph. You will need to build these packages yourself before you are able
to build this Python interface.
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 leidenalg
package is programmed in C++. Note that there are build scripts included in
the scripts/ directory. These are also used to build the binary wheels.
igraph (version >= 0.10). You can use
the file build_igraph.sh (on Unix-like systems) or build_igraph.bat
(on Windows) in the scripts/ directory to do this. For more details, see
https://igraph.org/c/doc/igraph-Installation.html.libleidenalg (version >= 0.10). You
can use the file build_libleidenalg.sh (on Unix-like systems) or
build_libleidenalg.bat (on Windows) in the scripts/ directory to do
this. For more details, see https://github.com/vtraag/libleidenalg.python setup.py build and python setup.py install, or use pip install .You can check if all went well by running a variety of tests using python -m unittest.
In case of any problems, best to start over with a clean environment. Make sure
you remove the igraph and leidenalg package completely. Then, do a
complete reinstall starting from pip install leidenalg. In case you
installed from source, and built the C libraries of igraph and
libleidenalg yourself, remove them completely and rebuild and reinstall
them.
This is the Python interface for the C++ package libleidenalg. There are no
plans at the moment for developing an R interface to the package. However, there
have been various efforts to port the package to R. These typically do not offer
all available functionality or have some other limitations, but nonetheless may
be very useful. The available ports are:
Please refer to the documentation for more details on function calls and parameters.
This implementation is made for flexibility, but igraph nowadays also
includes an implementation of the Leiden algorithm internally. That
implementation is less flexible: the implementation only works on undirected
graphs, and only CPM and modularity are supported. It is likely to be
substantially faster though.
Just to get you started, below the essential parts. To start, make sure to import the packages:
import leidenalg 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 = leidenalg.find_partition(G, leidenalg.ModularityVertexPartition);
Source code: https://github.com/vtraag/leidenalg
Issue tracking: https://github.com/vtraag/leidenalg/issues
See the documentation on Implementation for more details on how to
contribute new methods.
Please cite the references appropriately in case they are used.
.. [1] Traag, V.A., Waltman. L., Van Eck, N.-J. (2018). From Louvain to
Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233.
10.1038/s41598-019-41695-z <http://dx.doi.org/10.1038/s41598-019-41695-z>_
.. [2] 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 <http://doi.org/10.1088/1742-5468/2008/10/P10008>_
.. [3] 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 <http://doi.org/10.1103/PhysRevE.69.026113>_
.. [4] Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of
community detection. Physical Review E, 74(1), 016110.
10.1103/PhysRevE.74.016110 <http://doi.org/10.1103/PhysRevE.74.016110>_
.. [5] 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 <http://doi.org/10.1103/PhysRevE.84.016114>_
.. [6] Traag, V. A., Krings, G., & Van Dooren, P. (2013). Significant scales in
community structure. Scientific Reports, 3, 2930. 10.1038/srep02930 <http://doi.org/10.1038/srep02930>_
.. [7] 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 <http://doi.org/10.1103/PhysRevE.92.022816>_
.. [8] 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 <http://doi.org/10.1103/PhysRevE.80.036115>_
.. [9] 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 <http://doi.org/10.1126/science.1184819>_
.. [10] Zanini, F., Berghuis, B. A., Jones, R. C., Robilant, B. N. di,
Nong, R. Y., Norton, J., Clarke, Michael F., Quake, S. R. (2019).
northstar: leveraging cell atlases to identify healthy and neoplastic
cells in transcriptomes from human tumors. BioRxiv, 820928.
10.1101/820928 <https://doi.org/10.1101/820928>_
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.
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