Community structure via short random walks — walktrap.community • igraph

walktrap.community() was renamed to cluster_walktrap() to create a more consistent API.

Usage

walktrap.community(
  graph,
  weights = NULL,
  steps = 4,
  merges = TRUE,
  modularity = TRUE,
  membership = TRUE
)

graph: The input graph, edge directions are ignored in directed graphs.
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. Larger edge weights increase the probability that an edge is selected by the random walker. In other words, larger edge weights correspond to stronger connections.
steps: The length of the random walks to perform.
merges: Logical scalar, whether to include the merge matrix in the result.
modularity: Logical scalar, whether to include the vector of the modularity scores in the result. If the membership argument is true, then it will always be calculated.
membership: Logical scalar, whether to calculate the membership vector for the split corresponding to the highest modularity value.