leading.eigenvector.community: Community structure detecting based on the leading...

In igraph/rigraph: Network Analysis and Visualization

Community structure detecting based on the leading eigenvector of the community matrix

Description

leading.eigenvector.community() was renamed to cluster_leading_eigen() to create a more consistent API.

Usage

leading.eigenvector.community(
  graph,
  steps = -1,
  weights = NULL,
  start = NULL,
  options = arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame()
)

Arguments

graph	The input graph. Should be undirected as the method needs a symmetric matrix.
steps	The number of steps to take, this is actually the number of tries to make a step. It is not a particularly useful parameter.
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.
start	NULL, or a numeric membership vector, giving the start configuration of the algorithm.
options	A named list to override some ARPACK options.
callback	If not NULL, then it must be callback function. This is called after each iteration, after calculating the leading eigenvector of the modularity matrix. See details below.
extra	Additional argument to supply to the callback function.
env	The environment in which the callback function is evaluated.

igraph/rigraph documentation built on May 19, 2024, 6:19 a.m.