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  4. Algorithm 1044: PyGenStability, a Multiscale Community Detection with Generalized Markov Stability
 
research article

Algorithm 1044: PyGenStability, a Multiscale Community Detection with Generalized Markov Stability

Arnaudon, Alexis
•
Schindler, Dominik J.
•
Peach, Robert L.
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June 28, 2024
ACM Transactions on Mathematical Software

We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualization tools for unsupervised multiscale community detection in graphs. PyGenStability finds optimized partitions of a graph at different levels of resolution by maximizing the generalized Markov Stability quality function with the Louvain or Leiden algorithm. The package includes automatic detection of robust graph partitions and allows the flexibility to choose quality functions for weighted undirected, directed, and signed graphs and to include other user-defined quality functions.

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Type
research article
DOI
10.1145/3651225
Scopus ID

2-s2.0-85197263586

Author(s)
Arnaudon, Alexis

Imperial College London

Schindler, Dominik J.

Imperial College London

Peach, Robert L.

Universitätsklinikum Würzburg

Gosztolai, Adam  

École Polytechnique Fédérale de Lausanne

Hodges, Maxwell

Spotify AB

Schaub, Michael T.

Rheinisch-Westfälische Technische Hochschule Aachen

Barahona, Mauricio

Imperial College London

Date Issued

2024-06-28

Published in
ACM Transactions on Mathematical Software
Volume

50

Issue

2

Article Number

15

Subjects

Additional Key Words and PhrasesMultiscale community detection

•

generalized Markov Stability

•

graph clustering

•

graphs

•

Leiden algorithm

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Louvain algorithm

•

modularity

•

network science

•

Python

•

unsupervised learning

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTS2  
FunderFunding(s)Grant NumberGrant URL

EPSRC

EP/N014529/1

German Research Foundation

Project-ID 424778381-TRR 295

HFSP

LT000669/2020-C

Available on Infoscience
January 24, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/243381
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