How to Install and Uninstall python3-leidenalg Package on Ubuntu 21.10 (Impish Indri)

Last updated: January 11,2025

1. Install "python3-leidenalg" package

Please follow the guidance below to install python3-leidenalg on Ubuntu 21.10 (Impish Indri)

$ sudo apt update $ sudo apt install python3-leidenalg

2. Uninstall "python3-leidenalg" package

Learn how to uninstall python3-leidenalg on Ubuntu 21.10 (Impish Indri):

$ sudo apt remove python3-leidenalg $ sudo apt autoclean && sudo apt autoremove

3. Information about the python3-leidenalg package on Ubuntu 21.10 (Impish Indri)

Package: python3-leidenalg
Architecture: amd64
Version: 0.8.7-1
Priority: optional
Section: universe/python
Source: python-leidenalg
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 312
Depends: python3 (<< 3.10), python3 (>= 3.9~), python3-igraph, python3:any, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libigraph1 (>= 0.7.1), libstdc++6 (>= 5.2), python3-pkg-resources
Filename: pool/universe/p/python-leidenalg/python3-leidenalg_0.8.7-1_amd64.deb
Size: 89268
MD5sum: 9b4afc6f29619ba0d40b7c04612da494
SHA1: 13d8a0747eef477bba6d8b049cfe90c31df39065
SHA256: dc680185df720eb95e93bf9167b11f06fcb0387f77f047114c5ab71126d1bf4d
SHA512: 63d41c0ad4cc104f34b65ec5585a52f0708ccbdf6c0ec865bdc128c9109f6c3a0cd81c45534347eccf34e46af3108447a7d771111befc6e987e8814792f5e2a6
Homepage: https://github.com/vtraag/leidenalg
Description-en: Python3 implementation of the Leiden algorithm in C++
This package implements the Leiden algorithm in C++ and exposes it to
Python. It relies on 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, 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.
Description-md5: bc44539c75b62da0ddc0dde5c29f5136