Leading Eigenvector Community Detection — community_leading

Detects communities with the leading eigenvector of the modularity matrix, splitting the network divisively. A directed graph is collapsed to an undirected graph with summed edge weights.

Usage

community_leading_eigenvector(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

com_le(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

Arguments

x: Network input.
weights: Edge weights. NULL uses the network weights and NA runs unweighted. Negative weights are replaced by their absolute values.
steps: Maximum number of split attempts. Default -1 (no limit).
start: Starting community structure (membership vector).
options: ARPACK options list. Default igraph::arpack_defaults().
callback: Optional function called after each split.
extra: Extra argument passed to callback.
env: Environment in which callback is evaluated.
...: Passed to to_igraph, whose only other argument is directed; anything else raises an "unused argument" error.

Value

A cograph_communities data frame with columns node and community. See communities for its attributes.

References

Newman, M.E.J. (2006). Finding community structure using the eigenvectors of matrices. Physical Review E, 74, 036104.

Examples

community_leading_eigenvector(regulation_net)
#> Community structure (leading_eigenvector)
#>   Nodes: 10  | Communities: 2  | Modularity: 0.1976 
#>   Sizes: 5, 5 
#> 
#>        node community
#>     Explore         1
#>        Plan         2
#>     Monitor         2
#>       Adapt         1
#>     Reflect         1
#>     Discuss         1
#>  Synthesize         1
#>    Evaluate         2
#>      Create         2
#>       Share         2