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
find_partition which finds the optimal partition using the
Leiden algorithm , which is an extension of the Louvain algorithm  for a
number of different methods. The methods currently implemented are (1)
modularity , (2) Reichardt and Bornholdt's model using the configuration
null model and the Erdös-Rényi null model , (3) the constant Potts model
(CPM) , (4) Significance , and finally (5) Surprise . In addition,
it supports multiplex partition optimisation allowing community detection on for
example negative links  or multiple time slices . There is the
possibility of only partially optimising a partition, so that some community
assignments remain fixed . It also provides some support for community
detection on bipartite graphs. See the documentation for more information.
pip install leidenalg. All major platforms are supported on
Python>=3.5, earlier versions of Python are no longer supported. Alternatively,
you can install from Anaconda (channel
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
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
package is programmed in
You can check if all went well by running a variety of tests using
There are basically two installation modes, similar to the python-igraph package itself (from which most of the setup.py comes).
Ccore library is installed yet. The
Ccore library of igraph that is provided within the
leidenalgpackage is compiled.
Ccore 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
In case of any problems, best to start over with a clean environment. Make sure
you remove the
python-igraph package completely, remove the
library and remove the
leidenalg package. Then, do a complete reinstall
pip install leidenalg. 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
leidenalg, 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.
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.
|||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|
|||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|
|||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|
|||Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74(1), 016110. 10.1103/PhysRevE.74.016110|
|||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|
|||Traag, V. A., Krings, G., & Van Dooren, P. (2013). Significant scales in community structure. Scientific Reports, 3, 2930. 10.1038/srep02930|
|||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|
|||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|
|||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|
|||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|
Copyright (C) 2020 V.A. Traag
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