NetworkToolbox (version 1.2.2)

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 none

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 comparmentalized

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. doi: 10.1088/1742-5468/2008/10/P10008

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52, 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003

Examples

# NOT RUN {
A <- TMFG(neoOpen)$A

modularity <- louvain(A)
# }