Community structure via greedy optimization of modularity

fastgreedy.community() was renamed to cluster_fast_greedy() to create a more consistent API.

Usage

fastgreedy.community(
  graph,
  merges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  weights = NULL
)

graph: The input graph. It must be undirected and must not have multi-edges.
merges: Logical scalar, whether to return the merge matrix.
modularity: Logical scalar, whether to return a vector containing the modularity after each merge.
membership: Logical scalar, whether to calculate the membership vector corresponding to the maximum modularity score, considering all possible community structures along the merges.
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.