Louvain Community Detection — community

Multi-level modularity optimization with the Louvain algorithm. The graph must be undirected; a directed graph raises an igraph error, so a directed network is converted first, for example with to_undirected().

Usage

community_louvain(x, weights = NULL, resolution = 1, seed = NULL, ...)

com_lv(x, weights = NULL, resolution = 1, seed = NULL, ...)

Arguments

x: Network input.
weights: Edge weights. NULL uses the network weights and NA runs unweighted. Negative weights are replaced by their absolute values.
resolution: Resolution parameter. Higher values yield more communities. Default 1 (standard modularity).
seed: Random seed for reproducibility. Default NULL.
...: Passed to to_igraph, whose only other argument is directed; anything else raises an "unused argument" error.

Value

A cograph_communities data frame with columns node and community. See communities for its attributes.

References

Blondel, V.D., Guillaume, J.L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics, P10008.

Examples

community_louvain(to_undirected(regulation_net), seed = 1)
#> Community structure (louvain)
#>   Nodes: 10  | Communities: 2  | Modularity: 0.1852 
#>   Sizes: 5, 5 
#> 
#>        node community
#>     Explore         1
#>        Plan         2
#>     Monitor         2
#>       Adapt         1
#>     Reflect         1
#>     Discuss         1
#>  Synthesize         1
#>    Evaluate         2
#>      Create         2
#>       Share         2