Finding community structure by multi-level optimization of modularity

multilevel.community() was renamed to cluster_louvain() to create a more consistent API.

Usage

multilevel.community(graph, weights = NULL, resolution = 1)

graph: The input graph. It must be undirected.
weights: The weights of the edges. It must be a positive numeric vector, NULL or NA. If it is NULL and the input graph has a ‘weight’ edge attribute, then that attribute will be used. If NULL and no such attribute is present, then the edges will have equal weights. Set this to NA if the graph was a ‘weight’ edge attribute, but you don't want to use it for community detection. A larger edge weight means a stronger connection for this function.
resolution: Optional resolution parameter that allows the user to adjust the resolution parameter of the modularity function that the algorithm uses internally. Lower values typically yield fewer, larger clusters. The original definition of modularity is recovered when the resolution parameter is set to 1.