louvain {NetworkToolbox}

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 <[e-mail]>

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)