infomap.community()
was renamed to cluster_infomap()
to create a more
consistent API.
Usage
infomap.community(
graph,
e.weights = NULL,
v.weights = NULL,
nb.trials = 10,
modularity = TRUE
)
Arguments
- graph
The input graph. Edge directions will be taken into account.
- e.weights
If not
NULL
, then a numeric vector of edge weights. The length must match the number of edges in the graph. By default the ‘weight
’ edge attribute is used as weights. If it is not present, then all edges are considered to have the same weight. Larger edge weights correspond to stronger connections.- v.weights
If not
NULL
, then a numeric vector of vertex weights. The length must match the number of vertices in the graph. By default the ‘weight
’ vertex attribute is used as weights. If it is not present, then all vertices are considered to have the same weight. A larger vertex weight means a larger probability that the random surfer jumps to that vertex.- nb.trials
The number of attempts to partition the network (can be any integer value equal or larger than 1).
- modularity
Logical scalar, whether to calculate the modularity score of the detected community structure.