How to Install and Uninstall python3-leidenalg Package on Kali Linux

Last updated: November 26,2024

1. Install "python3-leidenalg" package

Here is a brief guide to show you how to install python3-leidenalg on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                python3-leidenalg
            
                    
    
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2. Uninstall "python3-leidenalg" package

This is a short guide on how to uninstall python3-leidenalg on Kali Linux:


    
        
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                sudo apt remove
            
                        
                python3-leidenalg
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the python3-leidenalg package on Kali Linux

Package: python3-leidenalg
Source: python-leidenalg
Version: 0.10.2-1
Installed-Size: 264
Maintainer: Debian Med Packaging Team
Architecture: amd64
Depends: python3 (>= 3~), python3-igraph (>= 0.10), python3:any, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libigraph3 (>= 0.10.0), liblibleidenalg1 (>= 0.11.1), libstdc++6 (>= 13.1), python3-pkg-resources
Size: 50868
SHA256: a4fbcea129ba1feafc48ccd807f405a2c95719be182cd082ac20c2f7c7c1eb9f
SHA1: f7185ffa71d9d14a49fc48602a42b91d471e0d1b
MD5sum: 97cdf75a637f35bc8e8be0dd0e98f262
Description: 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:
Homepage: https://github.com/vtraag/leidenalg
Section: python
Priority: optional
Filename: pool/main/p/python-leidenalg/python3-leidenalg_0.10.2-1_amd64.deb