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louvain

Louvain Community Detection Algorithm

Description

Computes a vector of communities (community) and a global modularity measure (Q)

Usage

louvain(A, gamma, M0)

Arguments

A

An adjacency matrix of network data
gamma

Defaults to 1. Set to gamma > 1 to detect smaller modules and gamma < 1 for larger modules
M0

Input can be an initial community vector. Defaults to NULL

Value

Returns a list containing:

community

A community vector corresponding to each node's community
Q

Modularity statistic. A measure of how well the communities are compartmentalized

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

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, 2008, P10008.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

modularity <- louvain(A)
NetworkToolbox

Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

v1.4.1
GPL (>= 3.0)
Authors
Alexander Christensen [aut, cre] (<https://orcid.org/0000-0002-9798-7037>), Guido Previde Massara [ctb] (<https://orcid.org/0000-0003-0502-2789>)
Initial release
2020-12-07

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